Programs
| 3DMolMS | Hong Y., Li S., Welch C. J., Tichy S., Ye Y., Tang H., 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations. Bioinformatics, 2023, 39, Article No btad354. Abstract |
| 4-way Venn Diagram Generator | Author: Chris Seidel |
| ACFIS | Shi X.-X., Wang Z.-Z., Wang F., Hao G.-F., Yang G.-F., 2023, ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy. Nucleic Acids Research, 2023, 51, W25–W32. Abstract |
| ACID | Wang F., Wu F.-X., Li C.-Z., Jia C.-Y., Su S.-W., Hao G.-F., Yang G.-F., ACID: a free tool for drug repurposing using consensus inverse docking strategy. Journal of Cheminformatics, 2019, 11, Article No 73. Abstract |
| ACPYPE | Kagami L., Wilter A., Diaz A., Vranken V., The ACPYPE web server for small-molecule MD topology generation. Bioinformatics, 2023, 39, Article No btad350. Abstract |
| Activity Landscape Plotter | González-Medina M., Méndez-Lucio O., Medina-Franco J. L., Activity Landscape Plotter: a web-based application for the analysis of structure–activity relationships. Journal of Chemical Information and Modeling, 2017, 57, 397–402. Abstract |
| ADME SARfari | Davies M., Dedman N, Hersey A., Papadatos G., Hall M. D., Cucurull-Sanchez L., Jeffrey P., Hasan S., Eddershaw P. J., Overington J. P., ADME SARfari: comparative genomics of drug metabolizing systems. Bioinformatics, 2015, 31, 1695–1697. Abstract |
| ADMETlab | Dong J., Wang N.-N., Yao Z-J., Zhang L., Cheng Y., Ouyang D., Lu A.-P., Cao D.-S., ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 2018, 10, Article No 29. Abstract |
| ADMETlab 2.0 | Xiong G., Wu Z., Yi J., Fu L., Yang Z., Hsieh C., Yin M., Zeng X., Wu C., Lu A., Chen X., Hou T., Cao D., ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 2021, 49, W5–W14. Abstract |
| ADMETLab 3.0 | Fu L., Shi S., Yi J., Wang N., He Y., Wu Z., Peng J., Deng Y., Wang W., Wu C., Lyu A., Zeng X., Zhao W., Hou T., Cao D., ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Research, 2024, 52, W422–W431. Abstract |
| ADMETopt | Yang H., Sun L., Wang Z., Li W., Liu G., Tang Y., ADMETopt: a web server for ADMET optimization in drug design via scaffold hopping. Journal of Chemical Information and Modeling, 2018, 58, 2051–2056. Abstract |
| AdmetSAR | Yang H., Lou C., Sun L., Li J., Cai Y., Wang Z., Li W., Liu G., Tang Y., admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 2019, 35, 1067–1069. Abstract |
| AdmetSAR 3.0 | Gu Y., Yu Z., Wang Y., Chen L., Lou C., Yang C., Li W., Liu G., Tang Y., admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties. Nucleic Acids Research, 2024, 52, W432–W438. Abstract |
| ADVERPred | Ivanov S. M., Lagunin A. A., Rudik A. V., Filimonov D. A., Poroikov V. V., ADVERPred–web service for prediction of adverse effects of drugs. Journal of Chemical Information and Modeling, 2018, 58, 8–11. Abstract |
| Aggregator Advisor | Irwin J. J., Duan D., Torosyan H., Doak A. K., Ziebart K. T., Sterling T., Tumanian G., Shoichet B. K., An Aggregation Advisor for ligand discovery. Journal of Medicinal Chemistry, 2015, 58, 7076–7087. Abstract |
| Alkemio | Gijón-Correas J. A., Andrade-Navarro M. A., Fontaine J. F., Alkemio: association of chemicals with biomedical topics by text and data mining. Nucleic Acids Research, 2014, 42, W422–W429. Abstract |
| AlloFinder | Huang M., Song K., Liu X., Lu S., Shen Q., Wang R., Gao J., Hong Y., Li Q., Ni D., Xu J., Chen G., Zhang J., AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses. Nucleic Acids Research, 2018, 46, W451–W458. Abstract |
| ALOGPS | Tetko I. V., Bruneau P., Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. J. Pharm. Sci., 2004, 93, 3103-3110. Abstract |
| AMMOS2 | Labbé C., Pencheva T., Jereva D., Desvillechabrol D., Becot J., Villoutreix B. O., Pajeva I., Miteva M. A., AMMOS2: a web server for protein–ligand–water complexes refinement via molecular mechanics. Nucleic Acids Research, 2017, 45, W350–W355. Abstract |
| AnchorQuery | Koes D. R., Dömling A., Camacho C. J., AnchorQuery: Rapid online virtual screening for small-molecule protein–protein interaction inhibitors. Protein Science, 2018, 27, 229-232. Abstract |
| antiSMASH | Blin K., Shaw S., Augustijn H. E., Reitz Z. L., Biermann F., Alanjary M., Fetter A., Terlouw B. R., Metcalf W. W., Helfrich E. J. N., van Wezel G. P., Medema M. H., Weber T., antiSMASH 7.0: new and impr o ved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Research, 2023, 51, W46–W50. Abstract |
| ASFP | Zhang X., Shen C., Guo X., Wang Z., Weng G., Ye Q., Wang G., He Q., Yang B., Cao D., Hou T., ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions. Journal of Cheminformatics, 2021, 13, Article No 6. Abstract |
| Atomic Charge Calculator | Ionescu C.-M., Sehnal D., Falginella F. L., Pant P., Pravda L., Bouchal T., Svobodová Vařeková R., Geidl S., Koča J., Atomic charge calculator: interactive web-based calculation of atomic charges in large biomolecular complexes and drug-like molecules. Journal of Cheminformatics, 2015, 7, Article No 50. Abstract |
| Atomic Charge Calculator II | Schindler O., Raček T., Maršavelski A., Koča J., Berka K., Svobodová R., Optimized SQE atomic charges for peptides accessible via a web application. Journal of Cheminformatics, 13, Article No 45. Abstract |
| BATMAN-TCM | Kong X., Liu C., Zhang Z., Cheng M., Mei Z., Li X., Liu P., Diao L., Ma Y., Jiang P., Kong X., Nie S., Guo Y., Wang Z., Zhang X., Wang Y., Tang L., Guo S., Liu Z., Li D., BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Research, 2024, 52, D1110–D1120. Abstract |
| Bayesil | Ravanbakhsh S., Liu P., Bjordahl T. C., Mandal R., Grant J. R., Wilson M., Eisner R., Sinelnikov I., Hu X., Luchinat C., Greiner R., Wishart D. S., Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE, 2015, 10, Article No e0124219. Abstract |
| BDPServer | Provider: Centro Nacional de Biotecnologia, CSIC |
| BEERE | Yue Z., Willey C. D., Hjelmeland A. B., Chen J. Y., BEERE: a web server for biomedical entity expansion, ranking and explorations. Nucleic Acids Research, 2019, 47, W578–W586. Abstract |
| BiasNet | Sanchez J. E., KC G. B., Franco J., Allen W. J., Garcia J. D., Sirimulla S., BiasNet: a model to predict ligand bias toward GPCR signaling. J. Chem. Inf. Model., 2021, 61, 4190–4199. Abstract |
| BINANA | Young J., Garikipati N., Durrant J. D., BINANA 2: Characterizing receptor/ligand interactions in Python and JavaScript. Journal of Chemical Information and Modeling, 2022, 62, 753–760. Abstract |
| BiNChE | Moreno P., Beisken S., Harsha B., Muthukrishnan V., Tudose I., Dekker A., Dornfeldt S., Taruttis F., Grosse I., Hastings J., Neumann S., Steinbeck C., BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology. BMC Bioinformatics, 2015, 16, Article No 56. Abstract |
| Binding Curve Viewer | Du Y., Binding Curve Viewer: visualizing the equilibrium and kinetics of protein−ligand binding and competitive binding. Journal of Chemical Information and Modeling, 2024, 64, 4180−4192. Abstract |
| Bioactivity-explorer | Liang L., Ma C., Du T., Zhao Y., Zhao X., Liu M., Wang Z., Lin J., Bioactivity-explorer: a web application for interactive visualization and exploration of bioactivity data. Journal of Cheminformatics, 2019, 11, Article No 47. Abstract |
| BioMet Toolbox | Garcia-Albornoz M., Thankaswamy-Kosalai S., Nilsson A., Väremo L., Nookaew I., Nielsen J., BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data. Nucleic Acids Research, 2014,42, W175–W181. Abstract |
| BioSilico | Hou B. K., Kim J. S., Jun J. H., Lee D.-Y., Kim Y. W., Chae S., Roh M., In Y.-H., Lee S. Y., BioSilico: an integrated metabolic database system. Bioinformatics, 2004, 20, 3270-3272. Abstract |
| BioStatFlow | Provider: INRA |
| Bio-TDS | Gnimpieba E. Z., VanDiermen M. S., Gustafson S. M., Conn B., Lushbough C. M., Bio-TDS: bioscience query tool discovery system. Nucleic Acids Research, 2017, 45, D1117–D1122. Abstract |
| BioTransformer | Wishart D. S., Tian S., Allen D., Oler E., Peters H., Lui V. W., Gautam V., Djoumbou-Feunang Y., Greiner R., Metz T. O., BioTransformer 3.0–a web server for accurately predicting metabolic transformation products. Nucleic Acids Research, 2022, 50, W115–W123. Abstract |
| BioTriangle | Dong J., Yao Z.-J., Wen M., Zhu M.-F., Wang N.-N., Miao H.-Y., Lu A.-P., Zeng W.-B., Cao D.-S., BioTriangle: a web‑accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions. Journal of Cheminformatics, 2016, 8, Article No 34. Abstract |
| BitterPredict | Dagan-Wiener A., Nissim I., Ben Abu N., Borgonovo G., Bassoli A., Niv M. Y., Bitter or not? BitterPredict, a tool for predicting taste from chemical structure. Scientific Reports, 2017, 7, Article No 12074. Abstract |
| BitterSweet Predict | Tuwani R., Wadhwa S., Bagler G., BitterSweet: building machine learning models for predicting the bitter and sweet taste of small molecules. Scientific Reports, 2019, 9, Article No 7155. Abstract |
| BitterX | Huang W., Shen Q., Su X., Ji M., Liu X., Chen Y., Lu S., Zhuang H., Zhang J., BitterX: a tool for understanding bitter taste in humans. Scientific Reports, 2016, 6, Article No 23450. Abstract |
| BMaps | Bryan D. R., Kulp J. L. Jr., Mahapatra M. K., Bryan R. L., Viswanathan U., Carlisle M. N., Kim S., Schutte W. D., Clarke K. V., Doan T. T., Kulp J. L. III, BMaps: A web application for fragment-based drug design and compound binding evaluation. Journal of Chemical Information and Modeling, 2023, 63, 4229–4236. Abstract |
| BoBER | Lešnik S., Škrlj B., Eržen N., Bren U., Gobec S., Konc J., Janežič D., BoBER: web interface to the base of bioisosterically exchangeable replacements. Journal of Cheminformatics, 2017, 9, Article No 62. Abstract |
| Breeze | Potdar S., Ianevski F., Ianevski A.,Tanoli Z., Wennerberg W., Seashore-Ludlow B., Kallioniemi O., Östling P., Aittokallio T., Saarela J., Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data. Nucleic Acids Research, 2023, 51, W57–W61. Abstract |
| CACTUS website | Sitzmann M., Filippov I.V., Nicklaus M.C., Internet resources integrating many small molecular databases. SAR QSAR in Environmental Research, 2008, 19, 1-9. Abstract |
| CADDIE | Hartung M., Anastasi E., Mamdouh Z. M., Nogales C., Schmidt H. H. H. W., Baumbach J., Zolotareva O., List M., Cancer driver drug interaction explorer. Nucleic Acids Research, 2022, 50, W138–W144. Abstract |
| CarcinoPred-EL | Zhang L., Ai H., Chen W., Yin Z., Hu H., Zhu J., Zhao J., Zhao Q., Liu H., CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Scientific Reports, 2017, 7, Article No 2118. Abstract |
| cardioToxCSM | Iftkhar S., de Sá A. G. C., Velloso J. P. L., Aljarf R., Pires D. E. V., Ascher D. B., cardioToxCSM: A web server for predicting cardiotoxicity of small molecules. Journal of Chemical Information and Modeling, 2022, 62, 4827-4836. Abstract |
| CAVE | Mao Z., Yuan Q., Li H., Zhang Y., Huang Y., Yang C., Wang R., Yang Y., Wu Y., Yang S., Liao X., Ma H., CAVE: a cloud-based platform for analysis and visualization of metabolic pathways. Nucleic Acids Research, 2023, 51, W70–W77. Abstract |
| CB-Dock | Liu Y., Grimm M., Dai W., Hou M., Xiao Z.-X., Cao Y., CB-Dock: a web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacologica Sinica, 2020, 41, 138–144. Abstract |
| CB-Dock2 | Liu Y., Yang X., Gan J., Chen S., Xiao Z.-X., Cao Y., CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research, 2022, 50, W159–W164. Abstract |
| CDPs | González-Medina M., Prieto-Martínez F. D., Owen J. R., Medina-Franco J. L., 2016, Consensus Diversity Plots: a global diversity analysis of chemical libraries. Journal of Cheminformatics, 2016, 8, Article No 63. Abstract |
| CDRUG | Li G.-H., Huang J.-F., CDRUG: a web server for predicting anticancer activity of chemical compounds. Bioinformatics, 2012, 28, 3334-3335. Abstract |
| CFM-ID | Wang F., Allen D., Tian S., Oler E., Gautam V., Greiner R., Metz T. O., Wishart D. S., CFM-ID 4.0 – a web server for accurate MS-based metabolite identification. Nucleic Acids Research, 2022, 50, W165–W174. Abstract |
| ChAIPred | Sharma N., Patiyal S., Dhall A., Devi N. L., Raghava G. P. S., ChAlPred: A web server for prediction of allergenicity of chemical compounds. Computers in Biology and Medicine, 2021, 136, Article No 104746. Abstract |
| CheckMyBlob | Brzezinski D., Porebski P. J., Kowiel M., Macnar J. M., Minor W., Recognizing and validating ligands with CheckMyBlob. Nucleic Acids Research, 2021, 49, W86–W92. Abstract |
| ChemBCPP | Dong J., Wang N.-N., Liu K.-Y., Zhu M.-F., Yun Y.-H., Zeng W.-B., Chen A. F., Cao D.-S., ChemBCPP: A freely available web server for calculating commonly used physicochemical properties. Chemometrics and Intelligent Laboratory Systems, 2017, 171, 65–73. Abstract |
| ChemBioNavigator | Stierand K., Harder T., Marek T., Hilbig M., Lemmen C., Rarey M., The internet as scientific knowledge base: Navigating the chem-bio space. Molecular Informatics, 2012, 31, 543-546. Abstract |
| ChemBioServer | Athanasiadis E., Cournia Z., Spyrou G., ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics, 2012, 28, 3002-3003. Abstract |
| ChEMBL Browser | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| ChemCalc | Patiny L., Borel A., ChemCalc: A building block for tomorrow’s chemical infrastructure. Journal of Chemical Information and Modeling, 2013, 53, 1223–1228. Abstract |
| ChemCompute | Provider: Sonoma State University |
| ChemDes | Dong J., Cao D.-S., Miao H.-Y., Liu S., Deng B.-C., Yun Y.-H., Wang N.-N., Lu A.-P., Zeng W.-B., Chen A. F., ChemDes: an integrated web‑based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics, 2015, 7, 60. Abstract |
| ChemFH | Shi S., Fu L., Yi J., Yang Z., Zhang X., Deng Y., Wang W., Wu C., Zhao W., Hou T., Zeng X., Lyu A., Cao D., ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery. Nucleic Acids Research, 2024, 52, W439–W449. Abstract |
| ChemFluo | Yang Z.-Y., Dong J., Yang Z.-J., Yin M., Jiang H.-L., Lu A.-P., Chen X., Hou T.-J., Cao D.-S., ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Briefings in Bioinformatics, 2021, 22, Article No bbaa282. Abstract |
| ChemFREE | Chen D., Liu Y., Liu Y., Zhao K., Zhang T., Gao Y., Wang Q., Song B., Hao G., ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world. Nucleic Acids Research, 2024, 52, W450–W460. Abstract |
| ChemGenerator | Yang J., Hou L., Liu K.-M., He W.-B, Cai Y., Yang F.-Q., Hu Y.-J., ChemGenerator: a web server for generating potential ligands for specific targets. Briefings in Bioinformatics, 2021, 22, Article No bbaa407. Abstract |
| Chemical Activity Predictor | Provider: National Institutes of Health |
| Chemical Identifier Resolver | Muresan S., Sitzmann M., Southan C., Mapping between databases of compounds and protein targets. Methods in Molecular Biology, 2012, 910, 145-164. Abstract |
| Chemical Structure Lookup | Sitzmann M., Filippov I.V., Nicklaus M.C., Internet resources integrating many small molecular databases. SAR QSAR in Environmental Research, 2008, 19, 1-9. Abstract |
| Chemicalize.org | Southan C., Stracz A., Extracting and connecting chemical structures from text sources using chemicalize.org. Journal of Cheminformatics, 2013, 5, Article No 20. Abstract |
| ChemicalToolbox | Bray S. A., Lucas X., Kumar A., Grüning B. A., The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform. Journal of Cheminformatics, 2020, 12, Article No 40. Abstract |
| Chemical Translation Service | Wohlgemuth G., Haldiya P. K., Willighagen E., Kind T., Fiehn O., The Chemical Translation Service – a web-based tool to improve standardization of metabolomic reports. Bioinformatics, 2010, 26, 2647–2648. Abstract |
| ChemMapper | Gong J., Cai C., Liu X., Ku X., Jiang H., Gao D., Li H., ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics, 2013, 29, 1827-1829. Abstract |
| ChemMaps | Borrel A., Conway M., Nolte S. Z., Unnikrishnan A., Schmitt C. P., Kleinstreuer N. C., ChemMaps.com v2.0: exploring the environmental chemical universe. Nucleic Acids Research, 2023, 51, W78–W82. Abstract |
| ChemMine | Backman T. W. H., Cao Y., Girke T., ChemMine tools: an online service for analyzing and clustering small molecules. Nucleic Acids Research, 2011, 39, W486–W491. Abstract |
| ChemMORT | Yi J.-C., Yang Z.-Y., Zhao W.-T., Yang Z.-J., Zhang X.-C., Wu C.-K., Lu A.-P., Cao D.-S., ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. Briefings in Bioinformatics, 2024, 25, Article No bbae008. Abstract |
| ChemoDOTS | Hoffer L., Charifi-Hoareau G., Barelier S., Betzi S., Miller T., Morelli X., Roche P., ChemoDOTS: a web server to design chemistry-driven focused libraries. Nucleic Acids Research, 2024, 52, W461–W468. Abstract |
| Chemotext | Capuzzi S. J., Thornton T. E., Liu K., Baker N., Lam W. I., O’Banion C. P., Muratov E. N., Pozefsky D., Tropsha A., Chemotext: a publicly available web server for mining drug–target–disease relationships in PubMed. Journal of Chemical Information and Modeling, 2018, 58, 212–218. Abstract |
| ChemSAR | Dong J., Yao Z.-J., Zhu M.-F., Wang N.-N., Lu B., Chen A. F., Lu A.-P., Miao H., Zeng W.-B., Cao D.-S., ChemSAR: an online pipelining platform for molecular SAR modeling. Journal of Cheminformatics, 2017, 9, Article No 27. Abstract |
| CIL | Grüning B. A., Senger C., Erxleben A., Flemming S., Günther S., Compounds In Literature (CIL): screening for compounds and relatives in PubMed. Bioinformatics, 2011, 27, 1341–1342. Abstract |
| Click2Drug | Provider: Swiss Institute of Bioinformatics |
| ClusPro | Porter K. A., Xia B., Beglov D., Bohnuud T., Alam N., Schueler-Furman O., Kozakov D., ClusPro PeptiDock: efficient global docking of peptide recognition motifs using FFT. Bioinformatics, 2017, 33, 3299–3301. Abstract |
| ClustVis | Metsalu T., Vilo J., ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Research, 2015, 43, W566–W570. Abstract |
| CMC | Liu L., Tsompana M., Wang Y., Wu D., Zhu L., Zhu R., Connection Map for Compounds (CMC): a server for combinatorial drug toxicity and efficacy analysis. Journal of Chemical Information and Modeling, 2016, 56, 1615–1621. Abstract |
| COACH | Yang J., Roy A., Zhang Y., Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 2013, 29, 2588-2595. Abstract |
| COACH-D | Wu Q., Peng Z., Zhang Y., Yang J., COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Research, 2018, 46, W438–W442. Abstract |
| CODD-Pred | Yin X., Wang X., Li Y., Wang J., Wang Y., Deng Y., Hou T., Liu H., Luo P., Yao X., CODD-Pred: A web server for efficient target identification and bioactivity prediction of small molecules. Journal of Chemical Information and Modeling, 2023, 63, 6169–6176. Abstract |
| Collector | López-Massaguer O., Sanz F., Pastor M., An automated tool for obtaining QSAR-ready series of compounds using semantic web technologies. Bioinformatics, 2018, 34, 131–133. Abstract |
| Convert | Provider: University of New Mexico |
| COPICAT | Sakakibara Y., Hachiya T., Uchida M., Nagamine N., Sugawara Y., Yokota M., Nakamura M., Popendorf K., Komori T., Sato K., COPICAT: a software system for predicting interactions between proteins and chemical compounds. Bioinformatics, 2012, 28, 745-746. Abstract |
| COSMOS | Sadowski P., Baldi P., Small-molecule 3D structure prediction using open crystallography data. Journal of Chemical Information and Modeling, 2013, 53, 3127–3130. Abstract |
| CovalentDock Cloud | Ouyang X. Zhou S., Ge Z., Li R., Kwoh C. K., CovalentDock Cloud: a web server for automated covalent docking. Nucleic Acids Research, 2013, 41, W329-W332. Abstract |
| CPRiL | Qaseem A., Günther S., CPRiL: compound–protein relationships in literature. Bioinformatics, 2022, 38, 4452–4453. Abstract |
| CRDD website | Provider: IMTECH/CSIR |
| CRDS | Lee A., Kim D., CRDS: consensus reverse docking system for target fishing. Bioinformatics, 2020, 36, 959–960. Abstract |
| CSI:FingerID | Dührkop K., Shen H., Meusel M., Rousu J., Böcker S., Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112, 12580–12585. Abstract |
| CSM-lig | Pires D. E. V., Ascher D. B., CSM-lig: a web server for assessing and comparing protein–small molecule affinities. Nucleic Acids Research, 2016, 44, W557–W561. Abstract |
| CSNAP | Lo Y.-C., Senese S., Li C.-M., Hu Q., Huang Y., Damoiseaux R., Torres J. Z., Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. PLoS Computational Biology, 2015, 11, Article No e1004153. Abstract |
| C-SPADE | Ravikumar B., Alam Z., Peddinti G., Aittokallio T., C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms. Nucleic Acids Research, 2017, 45, W495–W500. Abstract |
| CycloPs | Duffy F. J., Verniere M., Devocelle M., Bernard E., Shields D. C., Chubb A. J., CycloPs: generating virtual libraries of cyclized and constrained peptides including nonnatural amino acids. Journal of Chemical Information and Modeling, 2011, 51, 829-836. Abstract |
| DASPfind | Ba‑alawi W., Soufan O., Essack M., Kalnis P., Bajic V. B., DASPfind: new efficient method to predict drug–target interactions. Journal of Cheminformatics, 2016, 8, Article No 15. Abstract |
| dbCAN3 | Zheng J., Ge Q., Yan Y., Zhang X., Huang L., Yin Y., dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Research, 2023, 51, W115–W121. Abstract |
| DECIMER | Rajan K., Brinkhaus H. O., Sorokina M., Zielesny A., Steinbeck C., DECIMER‑Segmentation: Automated extraction of chemical structure depictions from scientific literature. Journal of Cheminformatics, 2021, 13, Article No 20. Abstract |
| DeepAR | Schaduangrat N., Anuwongcharoen N., Charoenkwan P., Shoombuatong W., DeepAR: a novel deep learning‑based hybrid framework for the interpretable prediction of androgen receptor antagonists. Journal of Cheminformatics, 2023, 15, Article No 50. Abstract |
| DeepFrag | Green H., Durrant J. D., DeepFrag: an open-source browser app for deep-learning lead optimization. Journal of Chemical Information and Modeling, 2021, 61, 2523-2529. Abstract |
| Deep-PK | Myung Y., de Sá A. G. C., Ascher D. B., 2024, Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Research, 2024, 52, W469–W475. Abstract |
| DeepScreening | Liu Z., Du J., Fang J., Yin Y., Xu G., Xie L., 2019, DeepScreening: a deep learning-based screening web server for accelerating drug discovery. Database, Article No baz104. Abstract |
| DeepSynergy | Preuer K., Lewis R. P. I., Hochreiter S., Bender A., Bulusu K. C., Klambauer G., DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics, 2018, 34, 1538–1546. Abstract |
| Dendrimer Builder | Provider: University of Bern |
| DIGREM | Zhang M., Lee S., Yao B., Xiao G., Xu L., Xie Y., DIGREM: an integrated web-based platform for detecting effective multi-drug combinations. Bioinformatics, 2019, 35, 1792–1794. Abstract |
| DINIES | Yamanishi Y., Kotera M., Moriya Y., Sawada R., Kanehisa M., Goto S., DINIES: drug–target interaction network inference engine based on supervised analysis. Nucleic Acids Research, 2014, 42, W39–W45. Abstract |
| DockThor | Santos K. B., Guedes I. A., Karl A. L. M., Dardenne L. E., Highly flexible ligand docking: benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. Journal of Chemical Information and Modeling, 2020, 60, 667-683. Abstract |
| D-Peptide Builder | Díaz-Eufracio B. I., Palomino-Hernández O., Arredondo-Sánchez A., Medina-Franco J. L., D-Peptide Builder: a web service to enumerate, analyze, and visualize the chemical space of combinatorial peptide libraries. Molecular Informatics, 2020, 39, Article No 2000035. Abstract |
| DPubChem | Soufan O., Ba-alawi W., Magana-Mora A., Essack M., Bajic V. B., DPubChem: a web tool for QSAR modeling and high-throughput virtual screening. Scientific Reports, 2018, 8, Article No 9110. Abstract |
| DrawGlycan-SFNG | Cheng K., Zhou Y., Neelamegham S., DrawGlycan-SNFG: a robust tool to render glycans and glycopeptides with fragmentation information. Glycobiology, 2017, 27, 200–205. Abstract |
| DrugBank MQN Browser | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| DrugBank SMIfp Browser | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| DrugComb | Zheng S., Aldahdooh J., Shadbahr T., Wang Y., Aldahdooh D., Bao J., Wang W., Tang J., DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal. Nucleic Acids Research, 2021, 49, W174–W184. Abstract |
| DrugE-Rank | Yuan Q., Gao J., Wu D., Zhang S., Mamitsuka H., Zhu S., DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics, 2016, 32, i18–i27. Abstract |
| DrugMint | Dhanda S. K., Singla D., Mondal A. K., Raghava G. P. S., DrugMint: a webserver for predicting and designing of drug-like molecules. Biology Direct, 2013, 8, Article No 28. Abstract |
| Drugmonizome | Kropiwnicki E., Evangelista J. E., Stein D. J., Clarke D. J. B., Lachmann A., Kuleshov M. V., Jeon M., Jagodnik K. M., Ma’ayan A., Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning. Database, 2021, Article No baab017. Abstract |
| DrugQuest | Papanikolaou N., Pavlopoulos G. A., Theodosiou T., Vizirianakis I. S., Iliopoulos I., DrugQuest – a text mining workflow for drug association discovery. BMC Bioinformatics, 2016, 17 (Suppl 5), Article No 182. Abstract |
| DrugRep | Gan J., Liu J., Liu Y., Chen S., Dai W., Xiao Z.-X., Cao Y., DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacologica Sinica, 2022, doi: 10.1038/s41401-022-00996-2. Abstract |
| Drug ReposER | Ab Ghani N. S., Ramlan E. I., Firdaus-Raih M., Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Research, 2019, 47, W350–W356. Abstract |
| Drugst.one | Maier A., Hartung M., Abovsky M., Adamowicz K., Bader G. D., Baier S., Blumenthal D. B., Chen J., Elkjaer M. L., Garcia-Hernandez C., Helmy M., Hoffmann M., Jurisica I., Kotlyar M., Lazareva O., Levi H., List M., Lobentanzer S., Loscalzo J., Malod-Dognin N., Manz Q., Matschinske J., Mee M., Oubounyt M., Pastrello C., Pico A. R., Pillich R. T., Poschenrieder J. M., Pratt D., Pržulj N., Sadegh S., Saez-Rodriguez J., Sarkar S., Shaked G., Shamir R., Trummer N., Turhan U., Wang R.-S., Zolotareva O., Baumbach J., Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing. Nucleic Acids Research, 2024, 52, W481–W488. Abstract |
| Drug Target Profiler | Tanoli Z., Alam Z., Ianevski A., Wennerberg K., Vähä-Koskela M., Aittokallio T., Interactive visual analysis of drug–target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Briefings in Bioinformatics, 2020, 21, 211–220. Abstract |
| EBI Search Engine | Park Y. M., Squizzato S., Buso N., Gur T., Lopez R., The EBI search engine: EBI search as a service-making biological data accessible for all. Nucleic Acids Research, 2017, 45, W545–W549. Abstract |
| EDock | Zhang W., E. W., Yin M., Zhang Y., EDock: blind protein–ligand docking by replica‑exchange Monte Carlo simulation. Journal of Cheminformatics, 2020, 12, Article No 37. Abstract |
| embryoTox | Aljarf R., Tang S., Pires D. E. V., Ascher D. B., embryoTox: Using graph-based signatures to predict the teratogenicity of small molecules. Journal of Chemical Information and Modeling, 2023, 63, 432–441. Abstract |
| eMolTox | Ji C., Svensson F., Zoufir A., Bender A., eMolTox: prediction of molecular toxicity with confidence. Bioinformatics, 2018, 34, 2508–2509. Abstract |
| Epigenetic Target Profiler | Sánchez-Cruz N., Medina-Franco J. L., Epigenetic Target Profiler: a web server to predict epigenetic targets of small molecules. Journal of Chemical Information and Modeling, 2021, 61, 1550–1554. Abstract |
| ePlatton | Du Y., Shi T., Ligand cluster-based protein network and ePlatton, a multi-target ligand finder. Journal of Cheminformatics, 2016, 8, Article No 23. Abstract |
| ESP | Kroll A., Ranjan S., Engqvist M. K. M., Lercher M. J., A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nature Communications, 2023, 14, Article No 2787. Abstract |
| EVenn | Chen T., Zhang H., Liu Y., Liu Y.-X., Huang L., EVenn: Easy to create repeatable and editable Venn diagrams and Venn networks online. Journal of Genetics and Genomics, 2021, 48, 863-866. Abstract |
| E-zyme | Yamanishi Y., Hattori M., Kotera M., Goto S., Kanehisa M., E-zyme: Predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics, 2009, 25, i179-i186. Abstract |
| FAERUN | Probst D., Reymond J.-L., FUn: A framework for interactive visualizations of large, high-dimensional datasets on the web. Bioinformatics, 2018, 34, 1433-1435. Abstract |
| FAF-Drugs4 | Lagorce D., Bouslama L., Becot J., Miteva M. A., Villoutreix B. O., FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics, 2017, 22, 3658–3660. Abstract |
| FAME 3 | Šícho M., Stork C., Mazzolari A., de Bruyn Kops C., Pedretti A., Testa B., Vistoli G., Svozil D., Kirchmair J., FAME 3: Predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes. Journal of Chemical Information and Modeling, 2019, 59, 3400-3412. Abstract |
| FEPrepare | Zavitsanou S., Tsengenes A., Papadourakis M., Amendola G., Chatzigoulas A., Dellis D., Cosconati S., Cournia Z., FEPrepare: a web-based tool for automating the setup of relative binding free energy calculations. Journal of Chemical Information and Modeling, 2021, 61, 4131-4138. Abstract |
| FINDSITEcomb2.0 | Zhou H., Cao H., Skolnick J., FINDSITEcomb2.0: A new approach for virtual ligand screening of proteins and virtual target screening of biomolecules. Journal of Chemical Information and Modeling, 2018, 58, 2343–2354. Abstract |
| FingerID | Dührkop K., Shen H., Meusel M., Rousu J., Böcker S., Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112, 12580–12585. Abstract |
| FishBAIT | Provider: Indian Institute of Technology Delhi |
| FitDock | Yang X., Liu Y., Gan J., Xiao Z.-X., Cao Y., FitDock: protein–ligand docking by template fitting. Briefings in Bioinformatics, 2022, 23, Article No bbac087. Abstract |
| FMM | Chou C. H., Chang W. C., Chiu C. M., Huang C. C., Huang H. D., FMM: a web server for metabolic pathway reconstruction and comparative analysis. Nucleic Acids Research, 2009, 37, W129-W134. Abstract |
| foodMASST | West K. A., Schmid R., Gauglitz J. M., Wang M., Dorrestein P. C., foodMASST a mass spectrometry search tool for foods and beverages. Science of Food, 2022, 6, Article No 22. Abstract |
| FormulationAI | Dong J., Wu Z., Xu H., Ouyang D., FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence. Briefings in Bioinformatics, 24, 25, Article No bbad419. Abstract |
| FragGrow | Zhang Y., Zhang Z., Ke D., Pan X., Wang X., Xiao X., Ji C., FragGrow: a web server for structure-based drug design by fragment growing within constraints. Journal of Chemical Information and Modeling, 2024, 64, 3970–3976. Abstract |
| Fragrance Browser | Ruddigkeit L., Awale M., Reymond J.-L., Expanding the fragrance chemical space for virtual screening. Journal of Cheminformatics, 2014, 6, Article No 27. Abstract |
| FragRep | Shan J., Pan X., Wang X., Xiao X., Ji C., FragRep: a web server for structure-based drug design by fragment replacement. Journal of Chemical Information and Modeling, 2020, 60, 5900-5906. Abstract |
| Galahad | Laenen G., Ardeshirdavani A., Moreau Y., Thorrez L., Galahad: a web server for drug effect analysis from gene expression. Nucleic Acids Research, 2015, 43, W208-W212. Abstract |
| Galaxy | The Galaxy Community, The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Research, 2024, 52, W83–W94. Abstract |
| Galaxy7TM | Lee G. R., Seok C., Galaxy7TM: flexible GPCR–ligand docking by structure refinement. Nucleic Acids Research, 2016, 44, W502–W506. Abstract |
| GalaxySagittarius | Yang J., Kwon S., Bae S.-H., Park K. M., Yoon C., Lee J.-H., Seok C., GalaxySagittarius: structure- and similarity-based prediction of protein targets for druglike compounds. Journal of Chemical Information and Modeling, 2020, 60, 3246–3254. Abstract |
| GCMS-ID | Wakoli J., Anjum A., Sajed T., Oler E., Wang F., Gautam V., LeVatte M., Wishart D. S., GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments. Nucleic Acids Research, 2024, 52, W381–W389. Abstract |
| GDA | Caroli J., Sorrentino G., Forcato M., Del Sal G., Bicciato S., GDA, a web-based tool for Genomics and Drugs integrated analysis. Nucleic Acids Research, 2018, 46, W148–W156. Abstract |
| GIANT | Kasahara K., Kinoshita K., GIANT: pattern analysis of molecular interactions in 3D structures of protein–small ligand complexes. BMC Bioinformatics, 15, Article No 12. Abstract |
| Glycam Web | Provider: University of Georgia |
| GlycanBuilder | Damerell D., Ceroni A., Maass K., Ranzinger R., Dell A., Haslam S. M., 2012, The GlycanBuilder and GlycoWorkbench glycoinformatics tools: updates and new developments. Biological Chemistry, 2012, 393, 1357-1362. Abstract Tsuchiya S., Aoki N. P., Shinmachi D., Matsubara M., Yamada I., Aoki-Kinoshita K. F., Narimatsu H., Implementation of GlycanBuilder to draw a wide variety of ambiguous glycans. Carbohydrate Research, 2017, 445, 104-116. Abstract |
| GlycoDigest | Gotz L., Abrahams J. L., Mariethoz J., Rudd P. M., Karlsson N. G., Packer N. H., Campbell M. P., Lisacek F., GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination. Bioinformatics, 2014, 30, 3131-3133. Abstract |
| GlycoFragments | Lohmann K.K., von der Lieth C.-W., GlycoFragment and GlycoSearchMS: web tools to support the interpretation of mass spectra of complex carbohydrates. Nucleic Acids Research, 2004, W261–W266. Abstract |
| GlycoQL | Hayes C., Daponte V., Mariethoz J., Lisacek F., This is GlycoQL. Bioinformatics, 2022, 38, Supplement 2, ii162–ii167. Abstract |
| GRASS | Kapaev R. R., Toukach P. V., GRASS: semi-automated NMR-based structure elucidation of saccharides. Bioinformatics, 2018, 34, 957–963. Abstract |
| Heatmapper | Babicki S., Arndt D., Marcu A., Liang Y., Grant J. R., Maciejewski A., Wishart D. S., Heatmapper: web-enabled heat mapping for all. Nucleic Acids Research, 2016, 44, W147–W153. Abstract |
| HemI | Ning W., Wei Y., Gao L., Han C., Gou Y., Fu S., Liu D., Zhang C., Huang X., Wu S., Peng D., Wang C., Xue Y., HemI 2.0: an online service for heatmap illustration. Nucleic Acids Research, 2022, 50, W405–W411. Abstract |
| HExpoChem | Taboureau O., Jacobsen U. P., Kalhauge C., Edsgärd D., Rigina O., Gupta R., Audouze K., HExpoChem: a systems biology resource to explore human exposure to chemicals. Bioinformatics, 2013, 29, 1231-1232. Abstract |
| Hit Dexter 2.0 | Stork C., Chen Y., Šícho M., Kirchmair J., Hit Dexter 2.0: machine-learning models for the prediction of frequent hitters. Journal of Chemical Information and Modeling, 2019, 59, 1030–1043. Abstract |
| HitPick | Hamad S., Adornetto G., Naveja J. J., Ravindranath A. C., Raffler J., Campillos M., HitPickV2: a web server to predict targets of chemical compounds. Bioinformatics, 2019, 35, 1239–1240. Abstract |
| HPLC Method Transfer Calculator | Provider: Sigma-Aldrich |
| IC50 Converter | Provider: Soongsil University |
| ICDrug | Wei M., Zhang X., Pan X., Wang B., Ji C., Qi Y., Zhang J. Z. H., HobPre: accurate prediction of human oral bioavailability for small molecules. Journal of Cheminformatics, 2022, 14, 1. Abstract Zhang X., Mao J., Wei M., Qi Y., Zhang J. Z. H., HergSPred: accurate classification of hERG blockers/nonblockers with machine-learning models. Journal of Chemical Information and Modeling, 2022, 62, 1830–1839. Abstract |
| iFIT | Petrič B., Goličnik M., Bavec A., iFIT: An automated web tool for determining enzyme-kinetic parameters based on the high-curvature region of progress curves. Acta Chimica Slovenica, 2022, 69, 478-482. Abstract |
| iMet | Aguilar-Mogas A., Sales-Pardo M., Navarro M., Guimerà R., Yanes O., iMet: a network-based computational tool to assist in the annotation of metabolites from tandem mass spectra. Analytical Chemistry, 2017, 89, 3474–3482. Abstract |
| InChI | Chemspider website |
| IntelliPatent | Wang P.-H., Tseng Y. J., IntelliPatent: a web‑based intelligent system for fast chemical patent claim drafting. Journal of Cheminformatics, 2019, 11, Article No 78. Abstract |
| InteractiVenn | Heberle H., Vaz Meirelles G., da Silva F. R., Telles G. P., Minghim R., InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics, 2015, 16, Article No 169. Abstract |
| Intercalate | Soni A., Khurana P., Singh T., Jayaram B., A DNA intercalation methodology for an efficient prediction of ligand binding pose and energetics. Bioinformatics, 2017, 33, 1488-1496. Abstract |
| IOCB RDF Platform | Galgonek J., Hurt T., Michlíková V., Onderka P., Schwarz J., Vondrášek J., Advanced SPARQL querying in small molecule databases. Journal of Cheminformatics, 2016, 8, Article No 31. Abstract |
| iPath | Darzi Y., Letunic I., Bork P., Yamada T., iPath3.0: interactive pathways explorer v3. Nucleic Acids Research, 2018, 46, W510–W513. Abstract |
| iRaPCA | Prada Gori D. N., Llanos M. A., Bellera C. L., Talevi A., Alberca L. N., iRaPCA and SOMoC: Development and validation of web applications for new approaches for the clustering of small molecules. Journal of Chemical Information and Modeling, 2022, 62, 2987-2998. Abstract |
| iScienceSearch | Kos A., Himmler H.-J., Efficient Internet searches for chemists. Chemical Informatics, 2015, 1, Article No 12. Abstract |
| ISDB | Bauer M. A., Belford R. E., Ding J., Berleant D., ISDB: Interaction Sentence Database. BMC Research Notes, 2010, 3, Article No 122. Abstract |
| KeggExp | Liu X., Han M., Zhao C., Chang C., Zhu Y., Ge C., Yin R., Zhan Y., Li C., Yu M., He F., Yang X., KeggExp: a web server for visual integration of KEGG pathways and expression profile data. Bioinformatics, 2019, 35, 1430–1432. Abstract |
| Ketcher demo | Karulin B., Kozhevnikov M., Ketcher: web-based chemical structure editor. Journal of Cheminformatics, 2011, 3 (Supplement 1), Poster P3. Abstract |
| KiDoQ | Garg A., Tewari R., Raghava G. P. S., KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials. BMC Bioinformatics, 2010, 11, Article No 125. Abstract |
| KinomeMETA | Li Z., Qu N., Zhou J., Sun J., Ren Q., Meng J., Wang G., Wang R., Liu J., Chen Y., Zhang S., Zheng M., Li X., KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning. Nucleic Acids Research, 2024, 52, W489–W497. Abstract |
| LAZAR | Provider: In Silico Toxicology GmbH |
| LIDeB Tools | Prada Gori D. N., Alberca L. N., Rodriguez S., Alice J. I., Llanos M. A., Bellera C. L., Talevi A., LIDeB Tools: A Latin American resource of freely available, open-source cheminformatics apps. Artificial Intelligence in the Life Sciences, 2022, 2, Article No 100049. Abstract |
| LigAdvisor | Pinzi L., Tinivella A., Gagliardelli L., Beneventano D., Rastelli G., LigAdvisor: a versatile and user-friendly web-platform for drug design. Nucleic Acids Research, 2021, 49, W326–W335. Abstract |
| LigDig | Fuller J. C., Martinez M., Henrich S., Stank A., Richter S., Wade R. C., LigDig: a web server for querying ligand–protein interactions. Bioinformatics, 2015, 31, 1147–1149. Abstract |
| LigQ | Radusky L., Ruiz-Carmona S., Modenutti C., Barril X., Turjanski A. G., Martí M. A., LigQ: a webserver to select and prepare ligands for virtual screening. Journal of Chemical Information and Modeling, 2017, 57, 1741–1746. Abstract |
| LigTMap | Shaikh F., Tai H. K., Desai N., Siu S. W. I., LigTMap: ligand and structure‑based target identification and activity prediction for small molecular compounds. Journal of Cheminformatics, 2021, 13, Article No 44. Abstract |
| LimTox | Cañada A., Capella-Gutierrez S., Rabal O., Oyarzabal J., Valencia A., Krallinger M., LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes. Nucleic Acids Research, 2017, 45, W484–W489. Abstract |
| LipidFinder | Alvarez-Jarreta J., Rodrigues P. R. S., Fahy E., O’Connor A., Price A., Gaud C., Andrews S., Benton P., Siuzdak G., Hawksworth J. I., Valdivia-Garcia M., Allen S. M., O’Donnell V. B., LipidFinder 2.0: advanced informatics pipeline for lipidomics discovery applications. Bioinformatics, 2021, 37, 1478–1479. Abstract |
| LipidMS | Alcoriza-Balaguer M. I., García-Cañaveras J. C., Ripoll-Esteve F. J., Roca M., Lahoz A., LipidMS 3.0: an R-package and a web-based tool for LC-MS/MS data processing and lipid annotation. Bioinformatics, 2022, 38, 4826–4828. Abstract |
| LipidSig | Liu C.-H., Shen P.-C., Lin W.-J., Liu H.-C., Tsai M.-H., Huang T.-Y., Chen I.-C., Lai Y.-L., Wang Y.-D., Hung M.-C., Cheng W.-C., LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Research, 2024, 52, W390–W397. Abstract |
| LipidSuite | Mohamed A., Hill M. M., LipidSuite: interactive web server for lipidomics differential and enrichment analysis. Nucleic Acids Research, 2021, 49, W346–W351. Abstract |
| LitSense | Allot A., Chen Q., Kim S., Vera Alvarez R., Comeau D. C., Wilbur W. J., Lu Z., LitSense: making sense of biomedical literature at sentence level. Nucleic Acids Research, 2019, 47, W594–W599. Abstract |
| LitSuggest | Allot A., Lee K., Chen Q., Luo L., Lu Z., LitSuggest: a web-based system for literature recommendation and curation using machine learning. Nucleic Acids Research, 2021, 49, W352–W358. Abstract |
| MAIP | Bosc N., Felix E., Arcila R., Mendez D., Saunders M. R., Green D. V. S., Ochoada J., Shelat A. A., Martin E. J., Iyer P., Engkvist O., Verras A., Duffy J., Burrows J., Gardner J. M. F., Leach A. R., MAIP: a web service for predicting bloodstage malaria inhibitors. Journal of Cheminformatics, 2021, 13, Article No 13. Abstract |
| MANORAA | Tanramluk D., Narupiyaku L., Akavipat R., Gong S., Charoensawan V., MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein–ligand fragment interaction, pathways and SNPs. Nucleic Acids Research, 2016, 44, W514–W521. Abstract |
| MAP4 | Capecchi A., Probst D., Reymond J.-L., One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of Cheminformatics, 2020, 12, Article No 43. Abstract |
| MAP-Search | Capecchi A., Probst D., Reymond J.-L., One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of Cheminformatics, 2020, 12, Article No 43. Abstract |
| MassSpecBlocks | Přívratský J., Novák J., MassSpecBlocks: a web‑based tool to create building blocks and sequences of nonribosomal peptides and polyketides for tandem mass spectra analysis. Journal of Cheminformatics, 2021, 13, Article No 51. Abstract |
| MBROLE2 | López-Ibáñez J., Pazos F., Chagoyen M., MBROLE 2.0–functional enrichment of chemical compounds. Nucleic Acids Research, 2016, 44, W201–W204. Abstract |
| MBROLE3 | López-Ibáñez J., Pazos F., Chagoyen M., MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis. Nucleic Acids Research, 2023, 51, W305–W309. Abstract |
| MecDDI | Hu W., Zhang W., Zhou Y., Luo Y., Sun X., Xu H., Shi S., Li T., Xu Y., Yang Q., Qiu Y., Zhu F., Dai H., MecDDI: clarified drug–drug interaction mechanism facilitating rational drug use and potential drug–drug interaction prediction. Journal of Chemical Information and Modeling, 2023, 63, 1626–1636. Abstract |
| MER | Couto F. M., Lamurias A., MER: a shell script and annotation server for minimal named entity recognition and linking. Journal of Cheminformatics, 2018, 10, Article No 58. Abstract |
| META-BOA | Hashimoto-Roth E., Surendra A., Lavallée-Adam M., Bennett S. A. L., Čuperlović-Culf M., METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance. Bioinformatics, 2022, 38, 5326–5327. Abstract |
| MetaboAnalyst | Pang Z., Chong J., Zhou G., de Lima Morais D. A., Chang L., Barrette M., Gauthier C., Jacques P.-E., Li S., Xia J., MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Research, 2021, 49, W388–W396. Abstract |
| Metabolome Searcher | Dhanasekaran A., Pearson J. L., Ganesan B., Weimer B. C., Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction. BMC Bioinformatics, 2015, 16, Article No 62. Abstract |
| MetaDock | Kamal I. M., Chakrabarti S., MetaDOCK: A combinatorial molecular docking approach. ACS Omega, 2023, 5850−5860. Abstract |
| MetaMapp | Barupal D. K., Haldiya P. K., Wohlgemuth G., Kind T., Kothari S. L., Pinkerton K. E., Fiehn O., MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinformatics, 2012, 13, Article No 99. Abstract |
| MetaPASS | Rudik A., Dmitriev A., Lagunin A., Filimonov D., Poroikov V., MetaPASS: a web application for analyzing the biological activity spectrum of organic compounds taking into account their biotransformation. Molecular Informatics, 2021, 40, Article No 2000231. Abstract |
| MetaPrint2D | Boyer S., Zamora I., New methods in predictive metabolism. Journal of Computer-Aided Molecular Design, 2002, 16, 403-413. Link |
| MetaTox | Rudik A. V., Bezhentsev V. M., Dmitriev A. V., Druzhilovskiy D. S., Lagunin A. A., Filimonov D. A., Poroikov V. V., MetaTox: web application for predicting structure and toxicity of xenobiotics’ metabolites. Journal of Chemical Information and Modeling, 2017, 57, 638–642. Abstract |
| MetATT | Xia J., Sinelnikov I. V. Wishart D. S., MetATT: a web-based metabolomics tool for analyzing time-series and two-factor datasets. Bioinformatics, 2011, 27, 2455-2456. Abstract |
| MetPA | Xia J., Wishart D. S., MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics, 2010, 26, 2342-2344. Abstract |
| MinePath | Koumakis L., Roussos P., Potamias G., Minepath.org: a free interactive pathway analysis web server. Nucleic Acids Research, 2017, 45, W116–W121. Abstract |
| ML-PLIC | Zhang X., Shen C., Wang T., Deng Y., Kang Y., Li D., Hou T., Pan P., ML-PLIC: a web platform for characterizing protein–ligand interactions and developing machine learning-based scoring functions. Briefings in Bioinformatics, 2023, 24, Article No bbad295. Abstract |
| MODEL | Li Z. R., Han L. Y., Xue Y., Yap C. W., Li H., Jiang L., Chen Y. Z., MODEL – Molecular Descriptor Lab: A web-based server for computing structural and physicochemical features of compounds. Biotechnology and Bioengineering, 2007, 97, 389-396. Abstract |
| MODLAB | Provider: Swiss Federal Institute of Technology |
| MolAlign | Brown B. P., Mendenhall J., Meiler J., BCL::MolAlign: three-dimensional small molecule alignment for pharmacophore mapping. Journal of Chemical Information and Modeling, 2019, 59, 689–701. Abstract |
| Molecular Anatomy | Manelfi C., Gemei M., Talarico C., Cerchia C., Fava A., Lunghini F., Beccari A. R., “Molecular Anatomy”: a new multi‑dimensional hierarchical scaffold analysis tool. Journal of Cheminformatics, 2021, 13, Article No 54. Abstract |
| Molecular Formats Converter | Provider: The Open University of Israel |
| MOLFEAT | Provider: National University of Singapore |
| MolGpka | Pan X., Wang H., Li C., Zhang J. Z. H., Ji C., MolGpka: a web server for small molecule pKa prediction using a graph-convolutional neural network. Journal of Chemical Information and Modeling, 2021, 61, 3159–3165. Abstract |
| MolHyb | Wang H., Pan X., Zhang Y., Wang X., Xiao X., Ji C., MolHyb: A web server for structure-based drug design by molecular hybridization. Journal of Chemical Information and Modeling, 2022, 62, 2916–2922. Abstract |
| Molinspiration | Ertl P., Rohde B., Selzer P., Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. Journal of Medicinal Chemistry, 2000, 43, 3714-3717. Abstract |
| Mol-Instincts | Provider: ChemEssen |
| MolModa | Kochnev Y., Ahmed M., Maldonado A. M., Durrant J. D., MolModa: accessible and secure molecular docking in a web browser. Nucleic Acids Research, 2024, 52, W498–W506. Abstract |
| MolOpt | Shan J., Ji C., MolOpt: a web server for drug design using bioisosteric transformation. Current Computer-Aided Drug Design, 2020, 16, 460-466. Abstract |
| MolSolv | Pan X., Zhao F., Zhang Y., Wang X., Xiao X., Zhang J. Z. H., Ji C., MolTaut: a tool for the rapid generation of favorable tautomer in aqueous solution. Journal of Chemical Information and Modeling, 2023, 63, 1833–1840. Abstract |
| MolTaut | Pan X., Zhao F., Zhang Y., Wang X., Xiao X., Zhang J. Z. H., Ji C., MolTaut: a tool for the rapid generation of favorable tautomer in aqueous solution. Journal of Chemical Information and Modeling, 2023, 63, 1833–1840. Abstract |
| MolView | Provider: Bergwerf Labs |
| Mordred | Moriwaki H., Tian Y.-S., Kawashita N., Takagi T., Mordred: a molecular descriptor calculator. Journal of Cheminformatics, 2018, 10, Article No 4. Abstract |
| MRE | Kuwahara H., Alazmi M., Cui X., Gao X., MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind. Nucleic Acids Research, 2016, 44, W217-W225. Abstract |
| MT-EpiPred | Zhang R., Xie X., Ni D., Wang H., Li J., Xiao W., MT-EpiPred: Multitask learning for prediction of small-molecule epigenetic modulators. Journal of Chemical Information and Modeling, 2024, 64, 110–118. Abstract |
| MTiOpenScreen | Labbé C., Rey J., Lagorce D., Vavruša M., Becot J., Sperandio O., Villoutreix B. O., Tufféry P., Miteva M. A., MTiOpenScreen: a web server for structure-based virtual screening. Nucleic Acids Research, 2015, 43, W448-W454. Abstract |
| MXFP Similarity Search | Capecchi A., Awale M., Probst D., Reymond J.-L., PubChem and ChEMBL beyond Lipinski. Molecular Informatics, 2019, 38, Article No 1900016. Abstract |
| MyCompoundId | Huan T., Li L., Counting missing values in a metabolite-intensity data set for measuring the analytical performance of a metabolomics platform. Analytical Chemistry, 2015, 87, 1306-1313. Abstract |
| MyMiner | Salgado D., Krallinger M., Depaule M., Drula E., Tendulkar A. V., Leitner F., Valencia A., Marcelle C., MyMiner: a web application for computer-assisted biocuration and text annotation. Bioinformatics, 2012, 28, 2285-2287. Abstract |
| NaPLeS | Sorokina M., Steinbeck C., NaPLeS: a natural products likeness scorer—web application and database. Journal of Cheminformatics, 2019, 11, Article No 55. Abstract |
| nCoVDock2 | Liu K., Lu X., Shi H., Xu X., Kong R., Chang S., nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands. Nucleic Acids Research, 2023, 51, W365–W371. Abstract |
| NERDD | Stork C., Embruch G., Šícho M., de Bruyn Kops C., Chen Y., Svozil D., Kirchmair J., NERDD: a web portal providing access to in silico tools for drug discovery. Bioinformatics, 2020, 36, 1291–1292. Abstract |
| NetInfer | Wu Z., Peng Y., Yu Z., Li W., Liu G., Tang Y., NetInfer: a web server for prediction of targets and therapeutic and adverse effects via network-based inference methods. Journal of Chemical Information and Modeling, 2020, 60, 3687–3691. Abstract |
| NOREVA | Yang Q., Wang Y., Zhang Y., Li F., Xia W., Zhou Y., Qiu Y., Li H. Zhu F., NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Research, 2020, 48, W436–W448. Abstract |
| Normalyzer | Chawade A., Alexandersson E., Levander F., Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. Journal of Proteome Research, 2014, 13, 3114–3120. Abstract |
| novoPathFinder | Ding S., Tian Y., Cai P., Zhang D., Cheng X., Sun D., Yuan L., Chen J., Tu W., Wei D.-Q., Hu Q.-N., novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model. Nucleic Acids Research, 2020, 48, W477–W487. Abstract |
| NP Analyst | Lee S., van Santen J. A., Farzaneh N., Liu D. Y., Pye C. R., Baumeister T. U. H., Wong W. R., Linington R. G., NP Analyst: An open online platform for compound activity mapping. ACS Central Science, 2022, 8, 223−234. Abstract |
| NP Navigator | Zabolotna Y., Ertl P., Horvath D., Bonachera F., Marcou G., Varnek A., NP Navigator: a new look at the natural product chemical space. Molecular Informatics, 2021, 40, Article No 2100068. Abstract |
| NP-Scout | Chen Y., Stork C., Hirte S., Kirchmair J., NP-Scout: machine learning approach for the quantification and visualization of the natural product-likeness of small molecules. Biomolecules, 2019, 9, Article No 43. Abstract |
| ODORactor | Liu X., Su X., Wang F., Huang Z., Wang Q., Li z., Zhang R., Wu L., Pan Y., Chen Y., Zhuang H., Chen G., Shi T., Zhang J., ODORactor: a web server for deciphering olfactory coding. Bioinformatics, 2011, 27, 2302-2303. Abstract |
| OmicsNet | Zhou G., Pang Z., Lu Y., Ewald J., Xia J., OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics. Nucleic Acids Research, 2022, 50, W527–W533. Abstract |
| OSIRIS | Provider: Organic Chemistry Portal |
| PaccMann | Cadow J., Born J., Manica M., Oskooei A., Rodríguez Martínez M., PaccMann: a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Research, 2020, 48, W502–W508. Abstract |
| PaintOmics | Liu T., Salguero P., Petek M., Martinez-Mira C., Balzano-Nogueira L., Ramšak Ž., McIntyre L., Gruden K., Tarazona S., Conesa A., PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases. Nucleic Acids Research, 2022, 50, W551–W559. Abstract |
| PanDrugs | Jiménez-Santos M. J., Nogueira-Rodríguez A., Piñeiro-Yáñez E., López-Fernández H., García-Martín S., Gómez-Plana P., Reboiro-Jato M., Gómez-López G., Glez-Peña D., Al-Shahrour F., PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data. Nucleic Acids Research, 2023, 51, W411–W418. Abstract |
| PASMet | Sriyudthsak K., Mejia R. F., Arita M., Yokota Hirai M., PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems. Nucleic Acids Research, 2016, 44, W205–W211. Abstract |
| Pathview Web | Luo W., Pant G., Bhavnasi Y. K., Blanchard S. G., Brouwer C., Pathview Web: user friendly pathway visualization and data integration. Nucleic Acids Research, 2017, 45, W501–W508. Abstract |
| PathwayConnector | Minadakis G., Zachariou M., Oulas A., Spyrou G. M., PathwayConnector: finding complementary pathways to enhance functional analysis. Bioinformatics, 2019, 35, 889–891. Abstract |
| PathWhiz | Pon A., Jewison T., Su Y., Liang Y., Knox C., Maciejewski A., Wilson M., Wishart D. S., Pathways with PathWhiz. Nucleic Acids Research, 2015, 43, W552–W559. Abstract |
| PBIT | Shende G., Haldankar H., Barai R. S., Bharmal M. H., Shetty V., Idicula-Thomas S., PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases. Bioinformatics, 2017, 33, 929-931. Abstract |
| PepSMI | Provider: NovoPro |
| PerMM | Lomize A. L., Hage J. M., Schnitzer K., Golobokov K., LaFaive M. B., Forsyth A. C., Pogozheva I. D., PerMM: a web tool and database for analysis of passive membrane permeability and translocation pathways of bioactive molecules. Journal of Chemical Information and Modeling, 2019, 59, 3094-3099. Abstract |
| PharmMapper | Wang X., Shen Y., Wang S., Li S., Zhang w., Liu X., Lai L., Pei J., Li H., PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Research, 2017, 45, W356–W360. Abstract |
| PhenoMeter | Carroll A., Zhang P., Whitehead L., Kaines S., Tcherkez G., Badger M. R., PhenoMeter: a metabolome database search tool using statistical similarity matching of metabolic phenotypes for high-confidence detection of functional links. Frontiers in Bioengineering and Biotechnology, 2015, 3, Article No 106. Abstract |
| Polypharmacology Browser | Awale M., Reymond J.-L., The polypharmacology browser: a web‑based multi‑fingerprint target prediction tool using ChEMBL bioactivity data. Journal of Cheminformatics, 2017, 9, Article No 11. Abstract |
| PolySearch | Liu Y., Liang Y., Wishart D., PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Research, 2015, 43, W535–W542. Abstract |
| PPB2 | Awale M., Reymond J.-L., Polypharmacology Browser PPB2: target prediction combining nearest neighbors with machine learning. Journal of Chemical Information and Modeling, 2019, 59, 10-17. Abstract |
| Pred-03 | Ollitrault G., Achebouche R., Dreux A., Murail S., Audouze K., Tromelin A., Taboureau O., Pred-O3, a web server to predict molecules, olfactory receptors and odor relationships. Nucleic Acids Research, 2024, 52, W507–W512. Abstract |
| PredDICTA | Shaikh S. A., Jayaram B., A Swift all-atom energy based computational protocol to predict DNA-drug binding affinity and ΔTm. Journal of Medicinal Chemistry, 2007, 50, 2240-2244. Abstract |
| Pred-hERG | Braga R. C., Alves V. M., Silva M. F. B., Muratov E., Fourches, D., Liao L. M., Tropsha A., Andrade C. H., Pred-hERG: A novel web-accessible computational tool for predicting cardiac toxicity. Molecular Informatics, 2015, 34, 698–701. Abstract |
| PrediSweet | Bouysset C., Belloir C., Antonczak S., Briand L., Fiorucci S., Novel scaffold of natural compound eliciting sweet taste revealed by machine learning. Food Chemistry, 2020, 324, Article No 126864. Abstract |
| Pred-Skin | Braga R. C., Alves V. M., Muratov E. N., Strickland J., Kleinstreuer N., Tropsha A., Horta Andrade C., Pred-Skin: a fast and reliable web application to assess skin sensitization effect of chemicals. Journal of Chemical Information and Modeling, 2017, 57, 1013–1017. Abstract |
| PRIMe | Sakurai T., Yamada Y., Sawada Y., Matsuda F., Akiyama K., Shinozaki K., Hirai M.Y., Saito K., PRIMe Update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation. Plant & Cell Physiology, 2013, 54, Article No e5. Abstract |
| PRISM 3 | Skinnider M. A., Merwin N. J., Johnston C. W., Magarvey N. A., PRISM 3: expanded prediction of natural product chemical structures from microbial genomes. Nucleic Acids Research, 2017, 45, W49–W54. Abstract |
| ProteinsPlus | Schöning-Stierand K., Diedrich K., Ehrt C., Flachsenberg F., Graef J., Sieg J., Penner P., Poppinga M., Ungethüm A., Rarey M., ProteinsPlus: a comprehensive collection of web-based molecular modeling tools. Nucleic Acids Research, 2022, 50, W611–W615. Abstract |
| ProTox 3.0 | Banerjee P., Kemmler E., Dunkel M., Preissner R., ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 2024, 52, W513–W520. Abstract |
| PubChem molecule editor | Provider: NCBI |
| PubChem MQN Browser | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| PubChem Promiscuity | Canny S. A., Cruz Y., Southern M. R., Griffin P. R., PubChem promiscuity: a web resource for gathering compound promiscuity data from PubChem. Bioinformatics, 2012, 28, 140-141. Abstract |
| PubChemQC | Nakata M., Shimazaki T., PubChemQC project: a large-scale first-principles electronic structure database for data-driven chemistry. Journal of Chemical Information and Modeling, 2017, 57, 1300-1308. Abstract |
| PubChem SMIfp Browser | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| pubmedKB | Li P.-H., Chen T.-F, Yu J.-Y., Shih S.-H., Su C.-H., Lin Y.-H., Tsai H.-K., Juan H.-F., Chen C.-Y., Huang J.-H., pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature. Nucleic Acids Research, 2022, 50, W616–W622. Abstract |
| PubTator | Wei C.-H., Allot A., Lai P.-T., Leaman R., Tian S., Luo L., Jin Q., Wang Z., Chen Q., Lu Z., PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research, 2024, 52, W540–W546. Abstract |
| PUG-REST | Kim S., Thiessen P. A., Cheng T., Yu B., Bolton E. E., An update on PUG-REST: RESTful interface for programmatic access to PubChem. Nucleic Acids Research, 2018, 46, W563–W570. Abstract |
| PUMA | González-Medina M., Medina-Franco J. L., Platform for Unified Molecular Analysis: PUMA. Journal of Chemical Information and Modeling, 2017, 57, 1735–1740. Abstract |
| QASDOM | Anashkina A. A., Kravatsky Y., Kuznetsov E., Makarov A. A., Adzhubei A. A., Meta-server for automatic analysis, scoring and ranking of docking models. Bioinformatics, 2018, 34, 297–299. Abstract |
| QueryChem | Klekota J., Roth F. P., Schreiber S. L., QueryChem: A google-powered web search combining text and chemical structures. Bioinformatics, 2006, 22, 1670-1673. Abstract |
| QsarDB | Ruusmann V., Sild S., Maran U., QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models. Journal of Cheminformatics, 2015, 7, Article No 32. Abstract |
| RASPD | Mukherjee G., Jayaram B., A rapid identification of hit molecules for target proteins via physico-chemical descriptors. Physical Chemistry Chemical Physics, 2013, 15, 9107-9116. Abstract |
| RASPD+ | Holderbach S., Adam L., Jayaram B., Wade R. C., Mukherjee G., RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features. Frontiers in Molecular Biosciences, 2020, 7, Article No 601065. Abstract |
| Reactome Diagram Viewer | Fabregat A., Sidiropoulos K., Viteri G., Marin-Garcia P., Ping P., Stein L., D’Eustachio P., Hermjakob H., Reactome diagram viewer: data structures and strategies to boost performance. Bioinformatics, 2018, 34, 1208–1214. Abstract |
| RealityConvert | Borrel A., Fourches D., RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality. Bioinformatics, 2017, 33, 3816–3818. Abstract |
| RealVS | Yin Y., Hu H., Yang Z., Xu H., Wu J., RealVS: Toward enhancing the precision of top hits in ligand-based virtual screening of drug leads from large compound databases. Journal of Chemical Information and Modeling, 2021, 61, 4924–4939. Abstract |
| REME | Shi Z., Wang D., Li Y., Deng R., Lin J., Liu C., Li H., Wang R., Zhao M., Mao Z., Yuan Q., Liao X., Ma H., REME: an integrated platform for reaction enzyme mining and evaluation. Nucleic Acids Research, 2024, 52, W299–W305. Abstract |
| RepExplore | Glaab E., Schneider R., RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis. Bioinformatics, 2015, 31, 2235–2237. Abstract |
| REStLESS | Chernyshov I. Y., Toukach P. V., REStLESS: automated translation of glycan sequences from residue-based notation to SMILES and atomic coordinates. Bioinformatics, 2018, 34, 2679–2681. Abstract |
| RetroBioCat | Finnigan W., Hepworth L. J., Flitsch S. L., Turner N. J., RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades. Nature Catalysis, 2021, 4, 98–104. Abstract |
| RF QSAR | Lee K., Lee M., Kim D., Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server. BMC Bioinformatics, 2017, 18, Article No 567. Abstract |
| RING | Del Conte A., Camagni G. F., Clementel D., Minervini G., Monzon A. M., Ferrari C., Piovesan D., Tosatto S. C. E., RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures. Nucleic Acids Research, 2024, 52, W306–W312. Abstract |
| RINGS | Akune Y., Hosoda M., Kaiya S., Shinmachi D., Aoki-Kinoshita K. F., The RINGS resource for glycome informatics analysis and data mining on the Web. OMICS, 2010, 14, 475-486. Abstract |
| RmSquare | Roy K., Chakraborty P., Mitra I., Ojha P. K., Kar S., Das R. N., Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data. Journal of Computational Chemistry, 2013, 34, 1071-1082. Abstract |
| RxnFinder | Hu Q.-N., Deng Z., Hu H., Ca D.-S., Liang Y.-Z., RxnFinder: biochemical reaction search engines using molecular structures, molecular fragments and reaction similarity. Bioinformatics, 2011, 27, 2465-2467. Abstract |
| S2DV | Shao J., Gong Q., Yin Z., Pan W., Pandiyan S., Wang L., S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules. Briefings in Bioinformatics, 2022, 23, Article No bbab593. Abstract |
| Sanjeevini | Jayaram B., Singh T., Mukherjee G., Mathur A., Shekhar S., Shekhar V., Sanjeevini: a freely accessible web-server for target directed lead molecule discovery. BMC Bioinformatics, 2012, 13 (Suppl 17), Article No S7. Abstract |
| SankeyMATIC | Author: Steve Bogart |
| SBGrid | Meyer P. A., Socias S., Key J., Ransey E., Tjon E. C., Buschiazzo A., Lei M., Botka C., Withrow J., Neau D., Rajashankar K., Anderson K.S., Baxter R. H., Blacklow S. C., Boggon T. J., Bonvin A. M., Borek D., Brett T. J., Caflisch A., Chang C. I., Chazin W. J., Corbett K. D., Cosgrove M. S., Crosson S., Dhe-Paganon S., Di Cera E., Drennan C. L., Eck M. J., Eichman B. F., Fan Q. R., Ferré-D’Amaré A. R., Fromme J. C., Garcia K. C., Gaudet R., Gong P., Harrison S. C., Heldwein E. E., Jia Z., Keenan R. J., Kruse A. C., Kvansakul M., McLellan J. S., Modis Y., Nam Y., Otwinowski Z., Pai E. F., Pereira P. J., Petosa C., Raman C. S., Rapoport T. A., Roll-Mecak A., Rosen M. K., Rudenko G., Schlessinger J., Schwartz T. U., Shamoo Y., Sondermann H., Tao Y. J., Tolia N. H., Tsodikov O. V., Westover K. D., Wu H., Foster I., Fraser J. S., Maia F. R., Gonen T., Kirchhausen T., Diederichs K., Crosas M., Sliz P., Data publication with the structural biology data grid supports live analysis. Nature Communications, 2016, 7, Article No 10882. Abstract |
| Screening Explorer | Empereur-Mot C., Zagury J.-F., Montes M., Screening Explorer–an interactive tool for the analysis of screening results. Journal of Chemical Information and Modeling, 2016, 56, 2281–2286. Abstract |
| SEA | Keiser M. J., Roth B. L., Armbruster B. N., Ernsberger P., Irwin J. J., Shoichet B. K., Relating protein pharmacology by ligand chemistry. Nature Biotechnology, 2007, 25, 197-206. Abstract |
| SEABED | Fenollosa C., Otón M., Andrio P., Cortés J., Orozco M., Goñi J. R., SEABED: Small molEcule activity scanner weB servicE baseD. Bioinformatics, 2015, 31, 773-775. Abstract |
| Selenzyme | Carbonell P., Wong J., Swainston N., Takano E., Nicholas J. Turner N. J., Scrutton N. S., Kell D. B., Breitling R., Faulon J.-L., Selenzyme: enzyme selection tool for pathway design. Bioinformatics, 2018, 34, 2153–2154. Abstract |
| SeMPI | Zierep P. F., Padilla N., Yonchev D. G., Telukunt K. K., Klementz D., Günther S. G., SeMPI: a genome-based secondary metabolite prediction and identification web server. Nucleic Acids Research, 2017, 45, W64-W71. Abstract |
| SensiPath | Delépine B., Libis V., Carbonell P., Faulon J.-L., SensiPath: computer-aided design of sensing-enabling metabolic pathways. Nucleic Acids Research, 2016, 44, W226–W231. Abstract |
| SerotoninAI | Łapińska N., Pacławski A., Szlęk J., Mendyk A., SerotoninAI: serotonergic system focused, artificial intelligence-based application for drug discovery. Journal of Chemical Information and Modeling, 2024, 64, 2150−2157. Abstract |
| Shiny | Buonaiuto M. A., Lang A. S. I. D., Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge. Chemistry Central Journal, 2015, 9, Article No 50. Abstract |
| SIMCOMP | Hattori M., Tanaka N., Kanehisa M., Goto S., SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Research, 2010, 38, W652-W656. Abstract |
| Skin Doctor | Provider: Universität Hamburg |
| SLAP | Chen B., Ding Y., Wild D. J., Assessing drug target association using semantic linked data. PLoS Computational Biology, 2012, 8, Article No e1002574. Abstract |
| SMARTSviewer | Schomburg K., Ehrlich H.-C., Stierand K., Rarey M., Chemical pattern visualization in 2D–the SMARTSviewer. Journal of Cheminformatics, 2011, 3 (Suppl 1), Abstract No O12. Abstract |
| Smiles2Monomers | Dufresne Y., Noé L., Leclère V., Pupin M., Smiles2Monomers: a link between chemical and biological structures for polymers. Journal of Cheminformatics, 2015, 7, Article No 6. Abstract |
| SMILESDrawer | Probst D., Reymond J.-L., SmilesDrawer: parsing and drawing SMILES-encoded molecular structures using client-side JavaScript. Journal of Chemical Information and Modeling, 2018, 58, 1–7. Abstract |
| SOMoC | Prada Gori D. N., Llanos M. A., Bellera C. L., Talevi A., Alberca L. N., iRaPCA and SOMoC: Development and validation of web applications for new approaches for the clustering of small molecules. Journal of Chemical Information and Modeling, 2022, 62, 2987-2998. Abstract |
| SPOT-Ligand2 | Litfin T., Zhou Y., Yang Y., SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library. Bioinformatics, 2017, 15, 1238-1240. Abstract |
| StackPR | Schaduangrat N., Anuwongcharoen N., Moni M. A., Lio’ P., Charoenkwan P., Shoombuatong W., StackPR is a new computational approach for large‑scale identification of progesterone receptor antagonists using the stacking strategy. Scientific Reports, 2022, 12, Article No 16435. Abstract |
| SUBCOMP | Hattori M., Tanaka N., Kanehisa M., Goto S., SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Research, 2010, 38, W652-W656. Abstract |
| Sugar Removal Utility | Schaub J., Zielesny A., Steinbeck C., Sorokina M., Too sweet: cheminformatics for deglycosylation in natural products. Journal of Cheminformatics, 2020, 12, article No 67. Abstract |
| SuperPred | Gallo K., Goede A., Preissner R., Gohlke B.-O., SuperPred 3.0: drug classification and target prediction–a machine learning approach. Nucleic Acids Research, 2022, 50, W726–W731. Abstract |
| SwissADME | Daina A., Michielin O., Zoete V., SwissADME: a free web tool to evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 2017, 7, Article No 42717. Abstract |
| SwissDock | Bugnon M., Röhrig U. F., Goullieux M., Perez M. A. S., Daina A., Michielin O., Zoete V., SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina. Nucleic Acids Research, 2024, 52, W324–W332. Abstract |
| SwissParam | Bugnon M., Goullieux M., Röhrig U. F., Perez M. A. S., Daina A., Michielin O., Zoete V., SwissParam 2023: A Modern Web-Based tool for efficient small molecule parametrization. Journal of Chemical Information and Modeling, 2023, 63, 6469–6475. Abstract |
| SwissSimilarity | Zoete V., Daina A., Bovigny C., Michielin O., SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening. Journal of Chemical Information and Modeling, 2016, 56, 1399–1404. Abstract |
| SwissTargetPrediction | Daina A., Michielin O., Zoete V., SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 2019, 47, W357–W364. Abstract |
| SynergyFinder | Ianevski A., Giri A. K., Aittokallio T., 2022, SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples. Nucleic Acids Research, 2022, 50, W739–W743. Abstract |
| systemsDock | Hsin K.-Y., Matsuoka Y., Asai Y., Kamiyoshi K., Watanabe T., Kawaoka Y., Kitano H., systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Research, 2016, 44, W507–W513. Abstract |
| TCMAnalyzer | Liu Z., Du J., Yan X., Zhong J., Cui L., Lin J., Zeng L., Ding P., Chen P., Zhou X., Zhou H., Gu Q., Xu J., TCMAnalyzer: a chemo- and bioinformatics web service for analyzing traditional Chinese medicine. Journal of Chemical Information and Modeling, 2018, 58, 550–555. Abstract |
| TeamTat | Islamaj R., Kwon D., Kim S., Lu Z., TeamTat: a collaborative text annotation tool. Nucleic Acids Research, 2020, 48, W5–W11. Abstract |
| TL4DTI | Dalkıran A., Atakan A., Rifaioğlu A. S., Martin M. J., Atalay R., Acar A. C., Doğan T., Atalay V., Transfer learning for drug–target interaction prediction. Bioinformatics, 2023, 39, i103-i110. Abstract |
| TNFPubChem | Melagraki G., Ntougkos E., Rinotas V., Papaneophytou C., Leonis G., Mavromoustakos T., Kontopidis G., Douni E., Afantitis A., Kollias G., Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB ligand (RANKL). PLoS Computational Biology, 2017, 13, Article No e1005372. Abstract |
| ToxAnalyzer | Rodrigues D. R., Batista Mariano D. C., Silva Santos L. H., Tagliati C. A., ToxAnalyzer: A user-friendly web tool for interactive data analysis and visualization of chemical compounds from the Comparative Toxicogenomics Database (CTD)™. Computational Toxicology, 2021, 19, Article No 100170. Abstract |
| toxFlow | Varsou D.-D., Tsiliki G., Nymark P., Kohonen P., Grafström R., Sarimveis H., toxFlow: a web-based application for read-across toxicity prediction using omics and physicochemical data. Journal of Chemical Information and Modeling, 2018, 58, 543–549. Abstract |
| ToxiPred | Mishra N. K., Singla D., Agarwal S., Open Source Drug Discovery Consortium, Raghava G. P. S., ToxiPred: a server for prediction of aqueous toxicity of small chemical molecules in T. pyriformis. Journal of Translational Toxicology, 2014, 1, 21-27. Abstract |
| TPACM4 | Mukherjee G., Patra N., Barua P., Jayaram B., A Fast empirical GAFF compatible partial atomic charge assignment scheme for modeling interactions of small molecules with biomolecular targets (TPACM4). Journal of Computational Chemistry, 2011, 32, 893-907. Abstract |
| University of Berne website | Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730. Abstract |
| USR-VS | Li H., Leung K.-S., Wong M.-H., Ballester P. J., USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques. Nucleic Acids Research, 2016, 44, W436–W441. Abstract |
| VAMMPIRE-LORD | Weber J., Achenbach J., Moser D., Proschak E., VAMMPIRE-LORD: A web server for straightforward lead optimization using matched molecular pairs. Journal of Chemical Information and Modeling, 2015, 55, 207–213. Abstract |
| VCCLAB | Tetko I. V., Gasteiger J. C, Todeschini R., Mauri A., Livingstone D., Ertl P., Palyulin V. A., Radchenko E. V., Zefirov N. S., Makarenko A. S., Tanchuk V. Y., Prokopenko V. V., Virtual Computational Chemistry Laboratory – Design and description. Journal of Computer-Aided Molecular Design, 2005, 19, 453-463. Abstract |
| VEGA On-line | Pedretti V., Villa L., Vistoli G., VEGA – An open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. Journal of Computer-Aided Molecular Design, 2004, 18, 167–173. Abstract |
| Venn | Provider: University of Ghent |
| Venny | Author: Juan Carlos Oliveros; Provider: Centro Nacional de Biotecnología, (CNB-CSIC) |
| Vir2Drug | Minadakis G., Tomazou M., Dietis N., Spyrou G. M., Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. Briefings in Bioinformatics, 2023, 24, Article No bbac536. Abstract |
| Virtual LogP | Gaillard P., Carrupt P. A., Testa B., Boudon A., Molecular lipophilicity potential, a tool in 3D QSAR: method and applications. Journal of Computer-Aided Molecular Design, 1994, 8, 83-96. Abstract |
| VirtualTaste | Fritz F., Preissner R., Banerjee P., VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds. Nucleic Acids Research, 2021, 49, W679–W684. Abstract |
| Virtuous Umami | Pallante L., Korfiati A., Androutsos L., Stojceski F., Bompotas A., Giannikos I., Raftopoulos C., Malavolta M., Grasso G., Mavroudi S., Kalogeras A., Martos V., Amoroso D., Piga D., Theofilatos K., Deriu M. A., Toward a general and interpretable umami taste predictor using a multi‑objective machine learning approach. Scientific Reports, 2022, 12, Article No 21735. Abstract |
| vsFilt | Gushchina I. V., Polenova A. M., Suplatov D. A., Švedas V. K., Nilov D. K., vsFilt: a tool to improve virtual screening by structural filtration of docking poses. Journal of Chemical Information and Modeling, 2020, 60, 3692–3696. Abstract |
| Way2Drug | Rudik A. V., Dmitriev A. V., Lagunin A. A., Filimonov D. A., Poroikov V. V., 2016, Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics. Journal of Cheminformatics, 2016, 8, Article No 68. Abstract |
| webDrugCS | Awale M., Reymond J.-L., Web‑based 3D‑visualization of the DrugBank chemical space. Journal of Cheminformatics, 2016, 8, Article No 25. Abstract |
| Webina | Kochnev Y., Hellemann E., Cassidy K. C., Durrant J. D., Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser. Bioinformatics, 2020, 36, 4513–4515. Abstract |
| webMolCS | Awale M., Probst D., Reymond J.-L., WebMolCS: a web-based interface for visualizing molecules in three-dimensional chemical spaces. Journal of Chemical Information and Modeling, 2017, 57, 643-649. Abstract |
| Wikipedia Chemical Structure Explorer | Ertl P., Patiny L., Sander T., Rufener C., Zasso M., Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia. Journal of Cheminformatics, 2015, 7, Article No 10. Abstract |
| WURCS | Matsubara M., Aoki-Kinoshita K. F., Aoki N. P., Yamada I., Narimatsu H., WURCS 2.0 update to encapsulate ambiguous carbohydrate structures. Journal of Chemical Information and Modeling, 2017, 57, 632–637. Abstract |
| XenoNet | Flynn N. R., Le Dang N., Ward M. D., Swamidass S. J., XenoNet: inference and likelihood of intermediate metabolite formation. Journal of Chemical Information and Modeling, 2020, 60, 3431–3449. Abstract |
| ZINC Browser | Awale M., Reymond J.-L., A multi-fingerprint browser for the ZINC database. Nucleic Acids Research, 2014, 42, W234–W239. Abstract |
| ZINC Express | Bobrowski T. M., Korn D. R., Muratov E. N., Tropsha A., ZINC Express: a virtual assistant for purchasing compounds annotated in the ZINC database. Journal of Chemical Information and Modeling, 2021, 61, 1033–1036. Abstract |
Last Updated on 15-11-2024 by Piotr Minkiewicz