{"id":171,"date":"2022-08-25T10:58:00","date_gmt":"2022-08-25T08:58:00","guid":{"rendered":"http:\/\/metachemibio.webgazel.pl\/?page_id=171"},"modified":"2026-04-27T14:41:00","modified_gmt":"2026-04-27T12:41:00","slug":"reviews","status":"publish","type":"page","link":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/reviews\/","title":{"rendered":"Reviews"},"content":{"rendered":"\n

Reviews<\/h4>\n\n\n\n

2006<\/strong><\/p>\n\n\n\n

Maldonado A. G., Doucet J. P., Petitjean M., Fan B.-Y., Molecular similarity and diversity in chemoinformatics: From theory to applications. Molecular Diversity, 2006, 10, 39-79.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2007<\/strong><\/p>\n\n\n\n

Degtyarenko K., Ennis M., Garavelli J. S., Good annotation practice for chemical data in biology. In Silico Biology, 2007, 7 (Supplement 2), 45-56.<\/td>Abstract<\/a><\/td><\/tr>
Scior T., Bernard P., Medina-Franco J. L., Maggiora G. M., Large compound databases for structure-activity relationships studies in drug discovery. Mini-Reviews in Medicinal Chemistry, 2007, 7, 851-860.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2008<\/strong><\/p>\n\n\n\n

Aoki-Kinoshita K. F., An introduction to bioinformatics for glycomics research. PLoS Computational Biology, 2008, 4, Article No e1000075.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2009<\/strong><\/p>\n\n\n\n

Mart\u00ednez-Mayorga K., Medina-Franco J. L., Chapter 2. Chemoinformatics \u2013 applications in food chemistry. Advances in Food and Nutrition Research, 2009, 58, 33-56.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2010<\/strong><\/p>\n\n\n\n

Ertl P., Molecular structure input on the web. Journal of Cheminformatics, 2010, 2, Article No 1.<\/td>Abstract<\/a><\/td><\/tr>
Fourches D., Muratov E., Tropsha A., Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling, 2010, 50, 1189\u20131204.<\/td>Abstract<\/a><\/td><\/tr>
Judson R., Public databases supporting computational toxicology. Journal of Toxicology and Environmental Health B: Critical Reviews, 2010, 13, 218-231.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2011<\/strong><\/p>\n\n\n\n

Guha R., Wiggins G. D., Wild D. J., Baik M., Pierce M. E., Fox G. C., Improving usability and accessibility of cheminformatics tools for chemists through cyberinfrastructure and education. In Silico Biology, 2011, 11, 41-60.<\/td>Abstract<\/a><\/td><\/tr>
Koutsoukas A., Simms B., Kirchmair J., Bond P. J., Whitmore A. V., Zimmer S., Young M. P., Jenkins J. L., Glick M., Glen R. C., Bender A., From in silico target prediction to multi-target drug design: Current databases, methods and applications. Journal of Proteomics, 2011, 74, 2554\u20132574.<\/td>Abstract<\/a><\/td><\/tr>
Muresan S., Petrov P., Southan C., Kjellberg M. J., Kogej T., Tyrchan C., Varkonyi P., Xie P. H., Making every SAR point count: The development of Chemistry Connect for the large-scale integration of structure and bioactivity data. Drug Discovery Today, 2011, 16, 1019-1030.<\/td>Abstract<\/a><\/td><\/tr>
O\u2019Boyle N. M., Guha R., Willighagen E. L., Adams S. E., Alvarsson J., Bradley J.-C., Filippov I. V., Hanson R. M., Hanwell M. D., Hutchison G. R., James C. A., Jeliazkova N., Lang A. S., Langner K. M., Lonie D. C., Lowe D. M., Pansanel J., Pavlov D., Spjuth O., Steinbeck C., Tenderholt A. L., Theisen K. J., Murray-Rust P., Open data, open source and open standards in chemistry: The Blue Obelisk five years on. Journal of Cheminformatics, 2011, 3, Article No 37.<\/td>Abstract<\/a><\/td><\/tr>
Scalbert A., Andres-Lacueva C., Arita M., Kroon P., Manach C., Urpi-Sarda M., Wishart D., Databases on food phytochemicals and their health-promoting effects. Journal of Agricultural and Food Chemistry, 2011, 59, 4331\u20134348.<\/td>Abstract<\/a><\/td><\/tr>
Subramaniam S., Fahy E., Gupta S., Sud M., Byrnes R. W., Cotter D., Dinasarapu A. R., Maurya M. R., Bioinformatics and systems biology of the lipidome. Chemical Reviews, 2011, 111, 6452-6490.<\/td>Abstract<\/a><\/td><\/tr>
Varnek A., Baskin I. I., Chemoinformatics as a theoretical chemistry discipline. Molecular Informatics, 2011, 30, 20\u201332.<\/td>Abstract<\/a><\/td><\/tr>
Vazquez M., Krallinger M., Leitner F., Valencia A., Text mining for drugs and chemical compounds: methods, tools and applications. Molecular Informatics, 2011, 30, 506\u2013519.<\/td>Abstract<\/a><\/td><\/tr>
Williams A. J., Ekins S., A quality alert and call for improved curation of public chemistry databases. Drug Discovery Today, 2011, 16, 747\u2013750.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2012<\/strong><\/p>\n\n\n\n

Malkaram S. A., Hassan Y. I., Zempleni J., Online tools for bioinformatics analyses in nutrition sciences. Advances in Nutrition, 2012, 3, 654-665.<\/td>Abstract<\/a><\/td><\/tr>
Moura Barbosa A. J., Del Rio A., Freely accessible databases of commercial compounds for high-throughput virtual screenings. Current Topics in Medicinal Chemistry, 2012, 12, 866-877.<\/td>Abstract<\/a><\/td><\/tr>
Reymond J.-L., Awale M., Exploring chemical space for drug discovery using the chemical universe database. ACS Chemical Neuroscience, 2012, 3, 649\u2013657.<\/td>Abstract<\/a><\/td><\/tr>
Wegner J. K., Sterling A., Guha R., Bender A., Faulon J.-L., Hastings J., O’Boyle N., Overington J., Van Vlijmen H., Willighagen E., Cheminformatics. Communications of the ACM, 2012, 55, 65-75.<\/td>Abstract<\/a><\/td><\/tr>
Wishart D. S., Chapter 3: Small Molecules and Disease. PLoS Computational Biology, 2012, 8, Article No e1002805.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2013<\/strong><\/p>\n\n\n\n

Bird C. L., Frey J. G., Chemical information matters: An e-research perspective on information and data sharing in the chemical sciences. Chemical Society Reviews, 2013, 42, 6754-6776.<\/td>Abstract<\/a><\/td><\/tr>
Chagoyen M., Pazos F., Tools for the functional interpretation of metabolomic experiments. Briefings in Bioinformatics. 2013, 14, 737-744.<\/td>Abstract<\/a><\/td><\/tr>
Chen B., Wild D. J., 2013, Practice and challenges of building a semantic framework for chemogenomics research. Molecular Informatics, 2013, 32, 1000\u20131008.<\/td>Abstract<\/a><\/td><\/tr>
Clark A. M., Williams A. J., Ekins S., Cheminformatics workflows using mobile apps. Chem-Bio Informatics Journal, 2013, 13, 1-18.<\/td>Abstract<\/a><\/td><\/tr>
Ellinger J. J., Chylla R. A., Ulrich E. L., Markley J. L., Databases and software for NMR-based metabolomics. Current Metabolomics, 2013, 1, 28-40.<\/td>Abstract<\/a><\/td><\/tr>
G\u00f3mez-P\u00e9rez A., Mart\u00ednez-Romero M., Rodr\u00edguez-Gonz\u00e1lez A., V\u00e1zquez G., V\u00e1zquez-Naya J. M., Ontologies in medicinal chemistry: current status and future challenges. Current Topics in Medicinal Chemistry, 2013, 13, 576-590.<\/td>Abstract<\/a><\/td><\/tr>
Gurulingappa H., Mudi A., Toldo L., Hofmann-Apitius M., Bhate J., Challenges in mining the literature for chemical information. RSC Advances, 2013, 3, 16194-16211.<\/td>Abstract<\/a><\/td><\/tr>
Hartler J., Tharakan R., K\u00f6feler H. C., Graham D. R., Thallinger G. G., Bioinformatics tools and challenges in structural analysis of lipidomics MS\/MS data. Briefings in Bioinformatics, 2013, 14, 375-390.<\/td>Abstract<\/a><\/td><\/tr>
Heller S., McNaught A., Stein S., Tchekhovskoi D., Pletnev I., InChI – the worldwide chemical structure identifier standard. Journal of Cheminformatics, 2013, 5, Article no 7.<\/td>Abstract<\/a><\/td><\/tr>
Holton T. A., Vijayakumar V., Khaldi N., Bioinformatics: Current perspectives and future directions for food and nutritional research facilitated by a Food-Wiki Database. Trends in Food Science and Technology, 2013, 34, 5-17.<\/td>Abstract<\/a><\/td><\/tr>
de la Iglesia D., Garcia-Remesal M., de la Calle G., Kulikowski C., Sanz F., Maojo V., The impact of computer science in molecular medicine: enabling high-throughput research. Current Topics in Medicinal Chemistry, 2013, 13, 526-575.<\/td>Abstract<\/a><\/td><\/tr>
Leontyev A., Baranov D., Massive open online courses in chemistry: A comparative overview of platforms and features. Journal of Chemical Education, 2013, 90, 1533-1539.<\/td>Abstract<\/a><\/td><\/tr>
Libman D., Huang L., Chemistry on the go: Review of chemistry apps on smartphones. Journal of Chemical Education, 2013, 90, 320\u2013325.<\/td>Abstract<\/a><\/td><\/tr>
Medina-Franco J. L., Advances in computational approaches for drug discovery based on natural products. Revista Latinoamericana de Quimica, 2013, 41, 95-110.<\/td>Abstract<\/a><\/td><\/tr>
Minkiewicz P., Mici\u0144ski J., Darewicz M., Bucholska J., Biological and chemical databases for research into the composition of animal source foods. Food Reviews International, 2013, 29, 321-351.<\/td>Abstract<\/a><\/td><\/tr>
Scheubert K., Hufsky F., B\u00f6cker S., 2013, Computational mass spectrometry for small molecules. Journal of Cheminformatics, 2013, 5, Article No 12.<\/td>Abstract<\/a><\/td><\/tr>
Seoane J. A., Lopez-Campos G., Dorado J., Martin-Sanchez F., New approaches in data integration for systems chemical biology. Current Topics in Medicinal. Chemistry, 2013, 13, 591-601.<\/td>Abstract<\/a><\/td><\/tr>
Singla D., Dhanda S. K., Chauhan J. S., Bhardwaj A., Brahmachari S. K., Open Source Drug Discovery Consortium, Raghava G. P. S., Open source software and web services for designing therapeutic molecules. Current Topics in Medicinal Chemistry, 2013, 13, 1172-1191.<\/td>Abstract<\/a><\/td><\/tr>
Wild D. J., Cheminformatics for the masses: a chance to increase educational opportunities for the next generation of cheminformaticians. Journal of Cheminformatics, 2013, 5, Article No 32.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2014<\/strong><\/p>\n\n\n\n

Bernard T., Bridge A., Morgat A., Moretti S., Xenarios I., Pagni M., Reconciliation of metabolites and biochemical reactions for metabolic networks. Briefings in Bioinformatics, 2014, 15, 123-135.<\/td>Abstract<\/a><\/td><\/tr>
Campbell M. P., Ranzinger R., L\u00fctteke T., Mariethoz J., Hayes C. A., Zhang J., Akune Y., Aoki-Kinoshita K. F., Damerell D., Carta G., York W. S., Haslam S. M., Narimatsu H., Rudd P. M., Karlsson N. G., Packer N. H., Lisacek F., Toolboxes for a stanadardised and systematic study of glycans. BMC Bioinformatics, 2014, 15 (Suppl. 1), Article No S9.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Eltyeb S., Salim N., Chemical named entities recognition: a review on approaches and applications. Journal of Cheminformatics, 2014, 6, Article No 17.<\/td>Abstract<\/a><\/td><\/tr>
Fearnley L. G., Davis M. J., Ragan M. A., Nielsen L. K., Extracting reaction networks from databases – opening Pandora\u2019s box. Briefings in Bioinformatics, 2014, 15, 973-983.<\/td>Abstract<\/a><\/td><\/tr>
Gasteiger J., Solved and unsolved problems of chemoinformatics. Molecular Informatics, 2014, 33, 454-457.<\/td>Abstract<\/a><\/td><\/tr>
Harvey M. J., Mason N. J., Rzepa H. S., Digital data repositories in chemistry and their integration with journals and electronic notebooks. Journal of Chemical Information and Modeling, 2014, 54, 2627\u20132635.<\/td>Abstract<\/a><\/td><\/tr>
Mart\u00ednez-Mayorga K., Medina-Franco J. L. (Editors), Foodinformatics. Applications of chemical information to food chemistry. Springer International Publishing AG, Cham, Switzerland, 2014.<\/td>Abstracts<\/a><\/td><\/tr>
McDonald A. G., Tipton K. F., Fifty-five years of enzyme classification: advances and difficulties. FEBS Journal, 2014, 281, 583\u2013592.<\/td>Abstract<\/a><\/td><\/tr>
de Souza A., Bittker J. A., Lahr D. L., Brudz S., Chatwin S., Oprea T. I., Waller A., Yang J. J., Southall N., Guha R., Schurer S. C., Vempati U. D., Southern M. R., Dawson E. S., Clemons P. A., Chung T. D. Y., An overview of the challenges in designing, integrating, and delivering BARD: a public chemical-biology resource and query portal for multiple organizations, locations, and disciplines. Journal of Biomolecular Screening, 2014, 19, 614-627.<\/td>Abstract<\/a><\/td><\/tr>
Stobbe M. D., Jansen G. A., Moerland P. D., van Kampen A. H. C., Knowledge representation in metabolic pathway databases. Briefings in Bioinformatics, 2014, 15, 455-470.<\/td>Abstract<\/a><\/td><\/tr>
Toropov A. A., Toropova A. P., Raska I., Leszczynska D., Leszczynski J., Comprehension of drug toxicity: Software and databases. Computers in Biology and Medicine, 2014, 45, 20\u201325.<\/td>Abstract<\/a><\/td><\/tr>
Willett P., The calculation of molecular structural similarity: principles and practice. Molecular Informatics, 2014, 33, 403\u2013413.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2015<\/strong><\/p>\n\n\n\n

Audouze K., Taboureau O., Chemical biology databases: from aggregation, curation to representation. Drug Discovery Today: Technology, 2015, 14, 25-29.<\/td>Abstract<\/a><\/td><\/tr>
Bawden D., Storing the wisdom: chemical concepts and chemoinformatics. Informatics, 2015, 2, 50-67.<\/td>Abstract<\/a><\/td><\/tr>
Bolton E., Reporting biological assay screening results for maximum impact. Drug Discovery Today: Technology, 2015, 14, 31-36.<\/td>Abstract<\/a><\/td><\/tr>
Cereto-Massagu\u00e9 A., Ojeda M. J., Valls C., Mulero M., Garcia-Vallv\u00e9 S., Pujadas G., Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71, 58\u201363.<\/td>Abstract<\/a><\/td><\/tr>
Cereto-Massagu\u00e9 A., Ojeda M. J., Valls C., Mulero M., Pujadas G., Garcia-Vallve S., Tools for in silico target fishing. Methods, 2015, 71, 98\u2013103.<\/td>Abstract<\/a><\/td><\/tr>
Danishuddin M., Khan A. U., Structure based virtual screening to discover putative drug candidates: Necessary considerations and successful case studies. Methods, 2015, 71, 135-145.<\/td>Abstract<\/a><\/td><\/tr>
Heller S. R., McNaught A., Pletnev I., Stein S., Tchekhovskoi D., InChI, the IUPAC International Chemical Identifier. Journal of Cheminformatics, 2015, 7, Article No 23.<\/td>Abstract<\/a><\/td><\/tr>
Hersey A., Chambers J., Bellis l., Bento A. P., Gaulton A., Overington J. P., Chemical databases: curation or integration by user-defined equivalence? Drug Discovery Today: Technologies, 2015, 14, 17-24.<\/td>Abstract<\/a><\/td><\/tr>
Iwaniak A., Minkiewicz P., Darewicz M., Protasiewicz M., Mogut D., Chemometrics and cheminformatics in the analysis of biologically active peptides from food sources. Journal of Functional Foods, 2015, 16, 334-351.<\/td>Abstract<\/a><\/td><\/tr>
Kos A., Himmler H.-J., Efficient Internet searches for chemists. Chemical Informatics, 2015, 1, Article No 12.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Lipinski C. A., Litterman N. K., Southan C., Williams A. J., Clark A. M., Ekins S., Parallel worlds of public and commercial bioactive chemistry data. Journal of Medicinal Chemistry, 2015, 58, 2068\u20132076.<\/td>Abstract<\/a><\/td><\/tr>
Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722\u2013730.<\/td>Abstract<\/a><\/td><\/tr>
Richter L., Ecker G. F., Medicinal chemistry in the era of big data. Drug. Discovery Today: Technologies, 2015, 14, 37\u201341.<\/td>Abstract<\/a><\/td><\/tr>
Southan C., 2015, Expanding opportunities for mining bioactive chemistry from patents. Drug Discovery Today: Technologies, 2015, 14, 3-9.<\/td>Abstract<\/a><\/td><\/tr>
Warr W. A., Many InChIs and quite some feat. Journal of Computer Aided Molecular Design, 2015, 29, 681\u2013694.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2016<\/strong><\/p>\n\n\n\n

Chen X., Yan C. C., Zhang X., Zhang X., Dai F., Yin J., Zhang Y., Drug\u2013target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics, 2016, 17, 696-712.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Dimova D., Bajorath J., Advances in activity cliff research. Molecular Informatics, 2016, 35, 181\u2013191.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Fourches D., Muratov E., Tropsha A., Trust, but verify II: a practical guide to chemogenomics data curation. Journal of Chemical Information and Modeling, 2016, 56, 1243\u20131252.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Gameiro D., P\u00e9rez-P\u00e9rez M., P\u00e9rez-Rodr\u00edguez G., Monteiro G., Azevedo N. F., Louren\u00e7o A., Computational resources and strategies to construct single-molecule metabolic models of microbial cells. Briefings in Bioinformatics, 2016, 17, 863-876.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Gasteiger J., Chemoinformatics: achievements and challenges, a personal view. Molecules, 2016, 21, Article No 151.<\/td>Abstract<\/a><\/td><\/tr>
Glaab E., Building a virtual ligand screening pipeline using free software: a survey. Briefings in Bioinformatics, 2016, 17, 352-366.<\/td>Abstract<\/a><\/td><\/tr>
Lavecchia A., Cerchia C., In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discovery Today, 2016, 21, 288-298.<\/td>Abstract<\/a><\/td><\/tr>
Lewis R., Deheuvels J., Ertl P., Pirard B., Sirockin F., Building compound archives for the future. Molecular Informatics, 2016, 35, 580\u2013582.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Li J., Zheng S., Chen B., Butte A. J., Swamidass S. J., Lu Z., A survey of current trends in computational drug repositioning. Briefings in Bioinformatics, 2016, 17, 2-12.<\/td>Abstract<\/a><\/td><\/tr>
Minkiewicz P., Darewicz M., Iwaniak A., Bucholska J., Starowicz P., Czyrko E., Internet databases of the properties, enzymatic reactions, and metabolism of small molecules-search options and applications in food science. International Journal of Molecular Sciences, 2016, 17, Article No 2039.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Misra B. P., van der Hooft J. J. J., Updates in metabolomics tools and resources: 2014\u20132015. Electrophoresis, 2016, 37, 86-110.<\/td>Abstract<\/a><\/td><\/tr>
Mohamed A., Nguyen C. H., Mamitsuka H., Current status and prospects of computational resources for natural product dereplication: a review. Briefings in Bioinformatics, 2016, 17, 309-321.<\/td>Abstract<\/a><\/td><\/tr>
Nongonierma A. B., FitzGerald R. J., Learnings from quantitative structure\u2013activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review. RSC Advances, 2016, 6, 75400\u201375413.<\/td>Abstract<\/a><\/td><\/tr>
Pehrsson P. R., Haytowitz D. B., Food composition databases. in: Caballero B., Finglas P. M., Toldr\u00e1 F. (Editors), Encyclopedia of Food and Health, Elsevier Ltd, 2016, pp 16-21.<\/td>Abstract<\/a><\/td><\/tr>
Przyby\u0142a P., Shardlow M., Aubin S., Bossy R., Eckart de Castilho R., Piperidis S., McNaught J., Ananiadou S., Text mining resources for the life sciences. Database, 2016, Article No 145.<\/td>Abstract<\/a><\/td><\/tr>
Puchades-Carrasco L., Palomino-Sch\u00e4tzlein M., P\u00e9rez-Rambla C., Pineda-Lucena A., Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Briefings in Bioinformatics, 2016, 17, 541-552.<\/td>Abstract<\/a><\/td><\/tr>
Raies A. B., Bajic V. B., In silico toxicology: computational methods for the prediction of chemical toxicity. WIREs Computational Molecular Science, 2016, 6, 147\u2013172.<\/td>Abstract<\/a><\/td><\/tr>
Senthilkumar B., Rajasekaran R., Computational resources for designing peptide based drugs preferred in the field of nanomedicine. Journal of Bionanoscience, 2016, 10, 1-14.<\/td>Abstract<\/a><\/td><\/tr>
Tetko I. V., Engkvist O., Koch U., Reymond J.-L., Chen H., BIGCHEM: Challenges and opportunities for big data analysis in chemistry. Molecular Informatics, 2016, 35, 615\u2013621.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Vinaixa M., Schymanski E. L., Neumann S., Navarro M., Salek R. M., Yanes O., Mass spectral databases for LC\/MS- and GC\/MS-based metabolomics: State of the field and future prospects. Trends in Analytical Chemistry, 2016, 78, 23\u201335.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2017<\/strong><\/p>\n\n\n\n

Abriata L. A., Web apps come of age for molecular sciences. Informatics, 2017, 4, Article No 28.<\/td>Abstract<\/a><\/td><\/tr>
Awale M., Visini R., Probst D., Ar\u00fas-Pous J., Reymond J.-L., 2017, Chemical space: Big data challenge for molecular diversity. Chimia, 71, 661-666.<\/td>Abstract<\/a><\/a><\/td><\/tr>
B\u00f6cker S., Searching molecular structure databases using tandem MS data: are we there yet? Current Opinion in Chemical Biology, 2017, 36, 1-6.<\/td>Abstract<\/a><\/td><\/tr>
Boezio B., Audouze K., Ducrot P., Taboureau O., Network-based approaches in pharmacology. Molecular Informatics, 2017, 36, Article No 1700048.<\/td>Abstract<\/a><\/td><\/tr>
Chen Y., de Bruyn Kops C., Kirchmair J., Data resources for the computer-guided discovery of bioactive natural products. Journal of Chemical Information and Modeling, 2017, 57, 2099-2111.<\/td>Abstract<\/a><\/a><\/td><\/tr>
De Vivo M., Cavalli A., Recent advances in dynamic docking for drug discovery. WIREs Computational Molecular Sciences, 2017, 7, Article No e1320.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Frainay C., Jourdan F., Computational methods to identify metabolic sub-networks based on metabolomic profiles. Briefings in Bioinformatics, 2017, 18, 43-56.<\/td>Abstract<\/a><\/td><\/tr>
Goldmann D., Zdrazil B., Digles D., Ecker G. F., Empowering pharmacoinformatics by linked life science data. Journal of Computer Aided Molecular Design, 2017, 31, 319\u2013328.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Gonz\u00e1lez-Medina M., Naveja J. J., S\u00e1nchez-Cruz N., Medina-Franco J. L., Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Advances, 2017, 7, 54153-54163.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Haug K., Salek R. M., Steinbeck C., Global open data management in metabolomics. Current Opinion in Chemical Biology, 2017, 36, 58\u201363.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Hawkins P. C. D., Conformation generation: the state of the art. Journal of Chemical Information and Modeling, 2017, 57, 1747\u20131756.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Houk K. N., Liu F., Holy grails for computational organic chemistry and biochemistry. Accounts of Chemical Research, 2017, 50, 539-543.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Kim S. M., Pe\u00f1a M. I., Moll M., Bennett G. N., Kavraki L. E., A review of parameters and heuristics for guiding metabolic pathfinding. Journal of Cheminformatics, 2017, 9, Article No 51.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Krallinger M., Rabal O., Louren\u00e7o A., Oyarzabal J., Valencia A., Information retrieval and text mining technologies for chemistry. Chemical Reviews, 2017, 117, 7673-7761.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Marvin H. J. P., Janssen E. M., Bouzembrak Y., Hendriksen P. J. M., Staats M., Big data in food safety: An overview, Critical Reviews in Food Science and Nutrition, 2017, 57, 2286-2295.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Minkiewicz P., Iwaniak A., Darewicz M., Annotation of peptide structures using SMILES and other chemical codes\u2013practical solutions. Molecules, 2017, 22, Article No 2075.<\/td>Abstract<\/a><\/td><\/tr>
Qiu T., Qiu J., Feng J., Wu D., Yang Y., Tang K., Cao Z., Zhu R., The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope. Briefings in Bioinformatics, 2017, 18, 125-136.<\/td>Abstract<\/a><\/td><\/tr>
Schymanski E. L., Ruttkies C., Krauss M., Brouard C., Kind T., D\u00fchrkop K., Allen F., Vaniya V., Verdegem D., B\u00f6cker S., Rousu J., Shen H., Tsugawa H., Sajed T., Fiehn F., Ghesqui\u00e8re B., Neumann S., Critical assessment of small molecule identification 2016: automated methods. Journal of Cheminformatics, 2017, 9, Article No 22.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Tetko I. V., Maran U., Tropsha A., Public (Q)SAR services, integrated modeling environments, and model repositories on the web: state of the art and perspectives for future development. Molecular Informatics, 2017, 36 (3), DOI: 10.1002\/minf.201600082.<\/td>Abstract<\/a><\/td><\/tr>
Urdidiales-Nieto D., Navas-Delgado I., Aldana-Montes J. F., Biological web service repositories review. Molecular Informatics, 2017, 36 (5-6), DOI: 10.1002\/minf.201600035.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Viant M. R., Kurland I. J., Jones M. R., Dunn W. B., How close are we to complete annotation of metabolomes? Current Opinion in Chemical Biology, 2017, 36, 64\u201369.<\/td>Abstract<\/a><\/td><\/tr>
Williams A. J., Pence H. E., The future of chemical information is now. Chemistry International, 2017, 39 (3), 9-14.<\/td>Abstract<\/a><\/td><\/tr>
Yuan S., Chan H. C. S., Hu Z., Using PyMOL as a platform for computational drug design. WIREs Computational Molecular Sciences, 2017, 7, Article No e1298.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2018<\/strong><\/p>\n\n\n\n

Agrawal P., Raghav P. K., Bhalla S., Sharma N., Raghava G. P. S., Overview of free software developed for designing drugs based on protein-small molecules interaction. Current Topics in Medicinal Chemistry, 2018, 18, 1146-1167.<\/td>Abstract<\/a><\/td><\/tr>
Allard P.-M., Bisson J., Azzollini A., Pauli G. F., Cordell G. A., Wolfender J.-L., Pharmacognosy in the digital era: shifting to contextualized metabolomics. Current Opinion in Biotechnology, 2018, 54, 57\u201364.<\/td>Abstract<\/a><\/td><\/tr>
Bla\u017eenovi\u0107 I., Kind T., Ji J., Fiehn O., Software tools and approaches for compound identification of LC-MS\/MS data in metabolomics. Metabolites, 2018, 8, Article No 31.<\/td>Abstract<\/a><\/td><\/tr>
Brown A. S., Patel C. J., A review of validation strategies for computational drug repositioning. Briefings in Bioinformatics, 19, 2018, 174\u2013177.<\/td>Abstract<\/a><\/td><\/tr>
Chen H., Kogej T., Engkvist O., Cheminformatics in drug discovery, an industrial perspective. Molecular Informatics, 2018, 37, Article No 1800041.<\/td>Abstract<\/a><\/td><\/tr>
Connolly Martin Y., How medicinal chemists learned about log P. Journal of Computer-Aided Molecular Design, 2018, 32, 809\u2013819.<\/td>Abstract<\/a><\/td><\/tr>
Do P.-C., Lee E. H., Le L., Steered molecular dynamics simulation in rational drug design. Journal of Chemical Information and Modeling, 2018, 58, 1473\u20131482.<\/td>Abstract<\/a><\/td><\/tr>
Engel T., Gasteiger J. (editors), Applied chemoinformatics: achievements and future opportunities, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2018<\/td>Abstracts<\/a><\/td><\/tr>
Fu D. Y., Meiler J., Predictive power of different types of experimental restraints in small molecule docking: a review. Journal of Chemical Information and Modeling, 2018, 58, 225\u2013233.<\/td>Abstract<\/a><\/td><\/tr>
Grimme S., Schreiner P. R., Computational chemistry: the fate of current methods and future challenges. Angewandte Chemie International Edition, 2018, 57, 4170-4176.<\/td>Abstract<\/a><\/td><\/tr>
Hessler G., Baringhaus K.-H., Artificial intelligence in drug design. Molecules, 2018, 23, Article No 2520.<\/td>Abstract<\/a><\/td><\/tr>
Katiyar R. S., Jha P. K., Molecular simulations in drug delivery: Opportunities and challenges. WIREs Computational Molecular Sciences. 2018, 8, Article No e1358.<\/td>Abstract<\/a><\/td><\/tr>
Musa A., Ghoraie L. S., Zhang S.-D., Glazko G., Yli-Harja O., Dehmer M., Haibe-Kains B., Emmert-Streib F., A review of connectivity map and computational approaches in pharmacogenomics. Briefings in Bioinformatics, 2018, 19, 506\u2013523.<\/td>Abstract<\/a><\/td><\/tr>
Naveja J. J., Oviedo-Osornio C. I., Trujillo-Minero N. N., Medina-Franco J.-L., Chemoinformatics: a perspective from an academic setting in Latin America. Molecular Diversity, 2018, 22, 247-258.<\/td>Abstract<\/a><\/td><\/tr>
Poongavanam V., Doak B. C., Kihlberg J., Opportunities and guidelines for discovery of orally absorbed drugs in beyond rule of 5 space. Current Opinion in Chemical Biology, 2018, 44, 23-29.<\/td>Abstract<\/a><\/td><\/tr>
Prieto-Mart\u00ednez F. D., Arciniega M., Medina-Franco J. L., Molecular docking: current advances and challenges. TIP Revista Especializada en Ciencias Qu\u00edmico-Biol\u00f3gicas, 2018, 21 (Supl. 1), 65-87.<\/td>Abstract<\/a>
<\/td><\/tr>
Raies A. B., Bajic V. B., In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data. WIREs Computational Molecular Science, 2018, 8, Article No e1352.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Scotti L., J\u00fanior F. J. B. M., Ishiki H. M., Ribeiro F. F., Duarte M. C., Santana G. S., Oliveira T. B., Diniz M. F. F. M., Quintans-J\u00fanior L. J., Scotti M. T., Computer-aided drug design studies in food chemistry.  in „Natural and artificial flavoring agents and food dyes”, Grumezescu A. M., Holban A. M.,  Elsevier B.V., 2018, pp 261-297.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Shahreza M. L., Ghadiri N., Mousavi S. R., Varshosaz J., Green J. R., A review of network-based approaches to drug repositioning. Briefings in Bioinformatics, 2018, 19, 878\u2013892.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Sotriffer C., Docking of covalent ligands: challenges and approaches. Molecular Informatics, 2018, 37, Article No 1800062.<\/td>Abstract<\/a><\/td><\/tr>
Tsugawa H., Advances in computational metabolomics and databases deepen the understanding of metabolisms. Current Opinion in Biotechnology 2018, 54, 10\u201317.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Vilar S., Friedman C., Hripcsak G., Detection of drug\u2013drug interactions through data mining studies using clinical sources, scientific literature and social media. Briefings in Bioinformatics, 2018, 19, 863\u2013877.<\/td>Abstract<\/a><\/td><\/tr>
Wingert B. M., Camacho C. J., Improving small molecule virtual screening strategies for the next generation of therapeutics. Current Opinion in Chemical Biology, 2018, 44, 87\u201392.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Winkler D. A., Sparse QSAR modelling methods for therapeutic and regenerative medicine. Journal of Computer-Aided Molecular Design, 2018, 32, 497\u2013509.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2019<\/strong><\/p>\n\n\n\n

Athar M., Sona A. N., Bekono B. D., Ntie-Kang F., Fundamental physical and chemical concepts behind \u201cdrug-likeness\u201d and \u201cnatural product-likeness\u201d. Physical Sciences Reviews, 2019, 4, Article No 20180101.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Awale M., Reymond J.-L., Web-Based tools for polypharmacology prediction. Methods in Molecular Biology, 2019, 1888, 255-272.<\/td>Abstract<\/a><\/td><\/tr>
Blanco-M\u00edguez A., Fdez-Riverola F., S\u00e1nchez B., Louren\u00e7o A., Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Briefings in Bioinformatics, 2019, 21, 1032-1056.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Byrne R., Schneider G., In silico target prediction for small molecules. Methods in Molecular Biology, 2019, 1888, 273-309.<\/td>Abstract<\/a><\/td><\/tr>
Chen G., Huang K., Miao M., Feng B., Campanella O. H., Molecular dynamics simulation for mechanism elucidation of food processing and safety: state of the art. Comprehensive Reviews in Food Science and Food Safety, 2019, 18, 243-263.<\/td>Abstract<\/a>
<\/td><\/tr>
Davis A. P., Wiegers J., Wiegers T. C., Mattingly C. J., Public data sources to support systems toxicology applications. Current Opinion in Toxicology, 2019, 16, 17\u201324.<\/td>Abstract<\/a><\/td><\/tr>
Gromski P. S., Henson A. B., Granda J. M., Cronin L., How to explore chemical space using algorithms and automation. Nature Reviews Chemistry, 2019, 3, 119\u2013128.<\/td>Abstract<\/a><\/td><\/tr>
Imai K., Ji D., Nwachukwu I. D., Agyei D., Udenigwe C. C., Bioinformatics and chemometrics for discovering biologically active peptides from food proteins. Reference Module in Food Science, 2019, doi: 10.1016\/B978-0-08-100596-5.22878-3<\/td>Abstract<\/a><\/a><\/td><\/tr>
Iwaniak A., Darewicz M., Mogut D., Minkiewicz P., Elucidation of the role of in silico methodologies in approaches to studying bioactive peptides derived from foods. Journal of Functional Foods, 2019, 61, Article No 103486.<\/td>Abstract<\/a><\/td><\/tr>
Mater A. C.,  Coote M. L., Deep learning in chemistry. Journal of Chemical Information and Modeling, 2019, 59, 2545-2559.<\/td>Abstract<\/a><\/td><\/tr>
Minkiewicz P., Tur\u0142o M., Iwaniak A., Darewicz M., Free accessible databases as a source of information about food components and other compounds with anticancer activity\u2013brief review. Molecules, 2019, 24, Article No 789.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Mu\u00f1oz E., Nov\u00e1\u010dek V., Vandenbussche P.-Y., Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models. Briefings in Bioinformatics, 2019, 20, 190\u2013202.<\/td>Abstract<\/a><\/a><\/td><\/tr>
P\u00e9rez-Sianes J., P\u00e9rez-S\u00e1nchez H., D\u00edaz F., Virtual screening meets deep learning. Current Computer-Aided Drug Design, 2019, 15, 6-28<\/td>Abstract<\/a><\/td><\/tr>
Rojas-Macias M. A., Mariethoz J., Andersson P., Jin C., Venkatakrishnan V., Aoki N. P., Shinmachi D., Ashwood C., Madunic K., Zhang T., Miller R. L., Horlacher O., Struwe W. B., Watanabe Y., Okuda S., Levander F., Kolarich D., Rudd P. M., Wuhrer M., Kettner C., Packer N. H., Aoki-Kinoshita K. F., Lisacek F., Karlsson N. G., Towards a standardized bioinformatics infrastructure for N- and O-glycomics. Nature Communications, 2019, 10, Article No 3275.<\/td>Abstract<\/a><\/td><\/tr>
Sachdev K., Gupta M. K., A comprehensive review of feature based methods for drug target interaction prediction. Journal of Biomedical Informatics, 2019, 93, article No 103159.<\/td>Abstract<\/a><\/td><\/tr>
Sieg J., Flachsenberg F., Rarey M., In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. Journal of Chemical Information and Modeling, 2019, 59, 947\u2013961.<\/td>Abstract<\/a><\/td><\/tr>
Shen C., Ding J., Wang Z., Cao D., Ding X., Hou T., From machine learning to deep learning: Advances in scoring functions for protein\u2013ligand docking. WIREs Computational Molecular Science. 2019, Article No e1429.<\/td>Abstract<\/a><\/td><\/tr>
da Silva Rocha, S. F. L., Olanda C. G., Fokoue H. H., Sant’Anna C. M. R., Virtual screening techniques in drug discovery: review and recent applications. Current Topics in Medicinal Chemistry, 2019, 19, 1751-1767.<\/td>Abstract<\/a><\/td><\/tr>
Sosnin S., Vashurina M., Withnall M., Karpov P., Fedorov M., Tetko I. V., A survey of multi-task learning methods in chemoinformatics. Molecular Informatics, 2019, 38, Article No 1800108.<\/td>Abstract<\/a><\/td><\/tr>
Sydow D., Burggraaff L., Szengel A., van Vlijmen H. W. T., Ijzerman A. P., van Westen G. J. P., Volkamer A., Advances and challenges in computational target prediction. Journal of Chemical Information and Modeling, 2019, 59, 1728-1742.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Toropov A. A., Toropova A. P., QSAR as a random event: criteria of predictive potential for a chance model. Structural Chemistry, 2019, 30, 1677-1683.<\/td>Abstract<\/a><\/td><\/tr>
Tuvi-Arad I., Blonder R., Technology in the service of pedagogy: Teaching with chemistry databases. Israel Journal of Chemistry, 2019, 59, 572-582.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2020<\/strong><\/p>\n\n\n\n

Ali N., Ullah S., Review to analyze and compare virtual chemistry laboratories for their use in education. Journal of Chemical Education, 2020, 97, 3563-3574.<\/td>Abstract<\/a><\/td><\/tr>
Chen G., Seukep A. J., Guo M., Recent advances in molecular docking for the research and discovery of potential marine drugs. Marine Drugs, 2020, 18, Article No 545.<\/td>Abstract<\/a><\/td><\/tr>
Chen Y., Kirchmair J., Cheminformatics in natural product-based drug discovery. Molecular Informatics, 2020, 39, Article No 2000171.<\/td>Abstract<\/a><\/td><\/tr>
David L., Thakkar A., Mercado R., Engkvist O., Molecular representations in AI\u2011driven drug discovery: a review and practical guide. Journal of Cheminformatics, 2020, 12, Article No 56.<\/td>Abstract<\/a><\/td><\/tr>
Hemmerich J., Ecker G. F., In silico toxicology: From structure\u2013activity relationships towards deep learning and adverse outcome pathways. WIREs Computational Molecular Sciences, 2020, 10, Article No e1475.<\/td>Abstract<\/a><\/td><\/tr>
Jarada T. N., Rokne J. G., Alhajj R., A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. Journal of Cheminformatics, 2020, 12, Article No 46.<\/td>Abstract<\/a><\/td><\/tr>
Journal of Chemical Education, special issue concerning chemistry teaching during Covid-19 pandemia<\/td>Abstracts<\/a><\/td><\/tr>
Li Q., Chapter 4 – Virtual screening of small-molecule libraries. in „Small Molecule Drug Discovery, Methods, Molecules and Applications”, Ed. by Trabocchi A., Lenci E., Elsevier Inc., 2020, pp 103-125.<\/td>Abstract<\/a><\/td><\/tr>
Martinez-Mayorga K., Madariaga-Mazon A., Medina-Franco J. L., Maggiora G., The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opinion on Drug Discovery, 2020, 15, 293-306.<\/td>Abstract<\/a><\/td><\/tr>
Medina-Franco J. L., Sald\u00edvar-Gonz\u00e1lez F. I., Cheminformatics to characterize pharmacologically active natural products. Biomolecules, 2020, 10, Article No 1566<\/td>Abstract<\/a><\/a><\/td><\/tr>
Muratov E. N., Bajorath J., Sheridan R. P., Filimonov D., Poroikov V., Oprea T. I., Baskin I. I., Varnek A., Roitberg A., Isayev O., Curtalolo S., Fourches D., Cohen Y., Aspuru-Guzik A., Winkler D. A., Agrafiotis D., Cherkasov A., Tropsha A., QSAR without borders. Chemical Society Reviews, 2020, 49, 3525-3564.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Nguyen-Vo T.-H., Nguyen L., Do N., Nguyen T.-N., Trinh K., Cao H., Le L., Plant metabolite databases: from herbal medicines to modern drug discovery. Journal of Chemical Information and Modeling, 2020, 60, 1101-1110.<\/td>Abstract<\/a><\/td><\/tr>
Ntie-Kang F., Svozil D., An enumeration of natural products from microbial, marine and terrestrial sources. Physical Sciences Reviews, 2020, 5, Article No 20180121.<\/td>Abstract<\/a><\/td><\/tr>
Paananen J., Fortino V., An omics perspective on drug target discovery platforms. Briefings in Bioinformatics, 2020, 21, 1937\u20131953.<\/td>Abstract<\/a><\/td><\/tr>
Rajan K., Brinkhaus H. O., Zielesny A., Steinbeck C., A review of optical chemical structure recognition tools. Journal of Cheminformatics, 2020, 12, Article No 60.<\/td>Abstract<\/a><\/td><\/tr>
Rodrigues T., Bernardes G. J. L., Machine learning for target discovery in drug development. Current Opinion in Chemical Biology, 2020, 56, 16\u201322.<\/td>Abstract<\/a><\/td><\/tr>
Sald\u00edvar\u2011Gonz\u00e1lez F. I., Huerta\u2011Garc\u00eda C. S., Medina-Franco J. L., Chemoinformatics\u2011based enumeration of chemical libraries: a tutorial. Journal of Cheminformatics, 2020, 12, Article No 64.<\/td>Abstract<\/a><\/td><\/tr>
Sald\u00edvar-Gonz\u00e1lez F. I., Medina-Franco J. L., Chapter 3 – Chemoinformatics approaches to assess chemical diversity and complexity of small molecules. in \u201cSmall Molecule Drug Discovery, Methods, Molecules and Applications\u201d, Ed. by Trabocchi A., Lenci E., Elsevier Inc., 2020, pp 83-102.<\/td>Abstract<\/a><\/td><\/tr>
Schaller D., \u0160ribar D., Noonan T., Deng L., Nguyen T. N., Pach S., Machalz D., Bermudez M., Wolber G., Next generation 3D pharmacophore modeling. WIREs Computational Molecular Science, 2020, 10, Article No e1468.<\/td>Abstract<\/a><\/td><\/tr>
Seidel T., Wieder O., Garon A., Langer T., Applications of the pharmacophore concept in natural product inspired drug design. Molecular Informatics, 2020, 39, Article No 202000059.<\/td>Abstract<\/a><\/td><\/tr>
Sorokina M., Steinbeck C., Review on natural products databases: where to find data in 2020. Journal of Cheminformatics, 2020, 12, Article No 20.<\/td>Abstract<\/a><\/td><\/tr>
Stumpfe D., Bajorath J., Current trends, overlooked Issues, and unmet challenges in virtual screening. Journal of Chemical Information and Modeling, 2020, 60, 4112\u20134115<\/td>Abstract<\/a><\/td><\/tr>
Tabei Y., Scalable prediction of compound-protein interaction on compressed molecular fingerprints. Molecular Informatics, 2020, 39, Article No 1900130.<\/td>Abstract<\/a><\/td><\/tr>
Tanoli Z., Alam Z., Ianevski A., Wennerberg K., V\u00e4h\u00e4-Koskela M., Aittokallio T., Interactive visual analysis of drug\u2013target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Briefings in Bioinformatics, 2020, 21, 211\u2013220.<\/td>Abstract<\/a><\/td><\/tr>
Tantillo D. J., Interrogating chemical mechanisms in natural products biosynthesis using quantum chemical calculations. WIREs Computational Molecular Science, 2020, 10, Article No e1453.<\/td>Abstract<\/a><\/td><\/tr>
Wang C., Kurgan L., Survey of similarity-based prediction of drug-protein interactions. Current Medicinal Chemistry, 2020, 27, 5856-5886.<\/td>Abstract<\/a><\/td><\/tr>
Zhang Y., Aryee A. N. A., Simpson B. K., 2020, Current role of in silico approaches for food enzymes. Current Opinion in Food Science, 2020, 31, 63\u201370.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2021<\/strong><\/p>\n\n\n\n

Aronskyy I., Masoudi-Sobhanzadeh Y., Cappuccio A., Zaslavsky E., Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discovery Today, 2021, 26, 2800-2815.<\/td>Abstract<\/a><\/td><\/tr>
Buntin K., Ertl P., Hoepfner D., Krastel P., Oakeley E. J., Pistorius D., Schuhmann T., Wong J., Petersen F., 2021, Deliberations on natural products and future directions in the pharmaceutical industry. Chimia, 75, 620-633.<\/td>Abstract<\/a><\/td><\/tr>
Carpio L. E., Sanz Y., Gozalbes R., Barigye S. J., Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review. Molecular Diversity, 2021, 25, 1425\u20131438.<\/td>Abstract<\/a><\/td><\/tr>
Chan L., Vasilevsky N., Thessen A., McMurry J., Haendel M., The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research. Database, 2021, Article No baab003.<\/td>Abstract<\/a><\/td><\/tr>
Gallegos L. C., Luchini G., St. John P. C., Kim S., Paton R. S., Importance of engineered and learned molecular representations in predicting organic reactivity, selectivity, and chemical properties. Accounts of Chemical Research, 2021, 54, 827-836.<\/td>Abstract<\/a><\/td><\/tr>
Goodman J. M., Pletnev I., Thiessen P., Bolton E., Heller S. R., InChI version 1.06: now more than 99.99% reliable. Journal of Cheminformatics, 2021, 13, Article No 40.<\/td>Abstract<\/a><\/td><\/tr>
Jorner K., Tomberg A., Bauer C., Sk\u00f6ld C., Norrby P.-O., Organic reactivity from mechanism to machine learning. Nature Reviews Chemistry, 2021, 5, 240\u2013255.<\/td>Abstract<\/a><\/td><\/tr>
L\u00f3pez-L\u00f3pez E., Bajorath J., Medina-Franco J. L., Informatics for chemistry, biology, and biomedical sciences. Journal of Chemical Information and Modeling, 2021, 61, 26-35.<\/td>Abstract<\/a><\/td><\/tr>
Luo H., Li M., Yang M., Wu F.-X., Li Y., Wang J., Biomedical data and computational models for drug repositioning: a comprehensive review. Briefings in Bioinformatics, 2021, 22, 1604\u20131619.<\/td>Abstract<\/a><\/td><\/tr>
Matsuzaka Y., Uesawa Y., A molecular image-based novel Quantitative Structure-Activity Relationship approach, deepsnap-deep learning and machine learning. Current Issues in Molecular Biology, 2021, 42, 455-472.<\/td>Abstract<\/a><\/td><\/tr>
Medina-Franco J. L., Martinez-Mayorga K., Fern\u00e1ndez-de Gortari E., Kirchmair J., Bajorath J., Rationality over fashion and hype in drug design. F1000Research, 2021, 10, Article No 397<\/td>Abstract<\/a><\/td><\/tr>
Molga K., Szymku\u0107 S., Grzybowski B. A., Chemist ex machina: advanced synthesis planning by computers. Accounts of Chemical Research, 2021, 54, 1094-1106.<\/td>Abstract<\/a><\/td><\/tr>
Mouchlis V. D., Afantitis A., Serra A., Fratello M., Papadiamantis A. G., Aidinis V., Lynch I., Greco D., Melagraki G., Advances in de novo drug design: from conventional to machine learning methods. International Journal of Molecular Sciences, 2021, 22, Article No 1676.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Muratov E. N., Amaro R., Andrade C. H., Brown N., Ekins S., Fourches D., Isayev O., Kozakov D., Medina-Franco J. L., Merz K. M., Oprea T. I., Poroikov V., Schneider G., Todd M. H., Varnek A., Winkler D. A., Zakharov A. V., Cherkasov A., Tropsha A., A critical overview of computational approaches employed for COVID-19 drug discovery. Chemical Society Reviews, 2021, 50, 9121-9151.<\/td>Abstract<\/a><\/a><\/td><\/tr>
Patil V. M., Masand N., Natural product databases and tools for anti-cancer drug discovery. Mini-Reviews in Medicinal Chemistry, 2021, 21, 2772-2785.<\/td>Abstract<\/a><\/td><\/tr>
P\u00e9rez Sant\u00edn E., Rodr\u00edguez Solana R., Gonz\u00e1lez Garc\u00eda M., Garc\u00eda Su\u00e1rez M. D. M., Blanco D\u00edaz G. D., Cima Cabal M. D., Moreno Rojas J. M., L\u00f3pez S\u00e1nchez J. I., Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIREs Computational Molecular Sciences., 2021, 11, Article No e1516.<\/td>Abstract<\/a><\/td><\/tr>
Singh N., Chaput L., Villoutreix B. O., Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Briefings in Bioinformatics, 2021, 22, 1790\u20131818.<\/td>Abstract<\/a><\/td><\/tr>
Tanoli Z, Seemab U., Scherer A., Wennerberg W., Tang J., V\u00e4h\u00e4-Koskela M., Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Briefings in Bioinformatics, 2021, 22, 1656\u20131678.<\/td>Abstract<\/a><\/td><\/tr>
Terayama K., Sumita M., Tamura R., Tsuda K., Black-box optimization for automated discovery. Accounts of Chemical Research, 2021, 54, 1334\u20131346.<\/td>Abstract<\/a><\/td><\/tr>
Tomasella C., Floris M., Guccione S., Pappalardo M., Basile L., Peptidomimetics in silico. Molecular Informatics, 2021, 40, Article No 2000087.<\/td>Abstract<\/a><\/td><\/tr>
Walters W. P., Barzilay R., Applications of deep learning in molecule generation and molecular property prediction. Accounts of Chemical Research, 2021, 54, 263\u2013270.<\/td>Abstract<\/a><\/td><\/tr>
Wilbraham L., Mehr S. H. M., Cronin L., Digitizing chemistry using the chemical processing unit: from synthesis to discovery. Accounts of Chemical Research, 2021, 54, 253-262.<\/td>Abstract<\/a><\/td><\/tr>
Yang S.-Q., Ye Q., Ding J.-J., Yin M.-Z., Lu A.-P., Chen X., Hou T.-J., Cao D.-S., Current advances in ligand-based target prediction. WIREs Computational Molecular Science, 2021, 11, Article No e1504.<\/td>Abstract<\/a><\/td><\/tr>
Zhao S., Su C., Lu Z., Wang F., Recent advances in biomedical literature mining. Briefings in Bioinformatics, 2021, 22, article No bbaa057.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2022<\/strong><\/p>\n\n\n\n

Abramov Y. A., Sun G., Zeng Q., Emerging landscape of computational modeling in pharmaceutical development. Journal of Chemical Information and Modeling, 2022, 62, 1160\u20131171.<\/td>Abstract<\/a><\/td><\/tr>
An X., Chen X., Yi D., Li H., Guan Y., Representation of molecules for drug response prediction. Briefings in Bioinformatics, 2022, 23, Article No bbab393.<\/td>Abstract<\/a><\/td><\/tr>
Bojar D., Lisacek F., Glycoinformatics in the artificial intelligence era. Chemical Reviews, 2022, 122, 15971\u201315988.<\/td>Abstract<\/a><\/td><\/tr>
Deng J., Yang Z., Ojima I., Samaras D., Wang F., Artificial intelligence in drug discovery: applications and techniques. Briefings in Bioinformatics, 2022, 23, Article No bbab430.<\/td>Abstract<\/a><\/td><\/tr>
Dhakal A., McKay C., Tanner T. J., Cheng J., Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions. Briefings in Bioinformatics, 2022, 23, Article No bbab476.<\/td>Abstract<\/a><\/td><\/tr>
Du B.-X., Qin Y., Jiang Y.-F., Xu Y., Yiu S.-M., Yu H., Shi J.-Y., Compound\u2013protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discovery Today, 2022, 27, 1350-1366.<\/td>Abstract<\/a><\/td><\/tr>
Ertl P., Gerebtzoff G., Lewis R., Muenkler H., Schneider N., Sirockin F., Stiefl N., Tosco P., Chemical reactivity prediction: current methods and different application areas. Molecular Informatics, 2022, 41, Article No 2100277.<\/td>Abstract<\/a><\/td><\/tr>
Fern\u00e1ndez-Torras A., Comajuncosa-Creus A., Duran-Frigola M., Aloy P., Connecting chemistry and biology through molecular descriptors. Current Opinion in Chemical Biology, 2022, 66, Article No 102090.<\/td>Abstract<\/a><\/td><\/tr>
Fombona-Pascual A., Fombona J., Vicente R. Augmented reality, a review of a way to represent and manipulate 3D chemical structures. Journal of Chemical Information and Modeling., 2022, 62, 1863\u20131872.<\/td>Abstract<\/a><\/td><\/tr>
Goel M., Bagler G., Computational gastronomy: A data science approach to food. Journal of Bioscience, 2022, 47, Article No 12.<\/td>Abstract<\/a><\/td><\/tr>
Gonzalez-Hernandez G., Krallinger M., Mu\u00f1oz M., Rodriguez-Esteban R., Uzuner \u00d6., Hirschman L., Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers. Database, 2022, Article No baac071.<\/td>Abstract<\/a><\/td><\/tr>
Hoffman S. C., Chenthamarakshan V., Wadhawan K., Chen P.-Y., Das P., Optimizing molecules using efficient queries from property evaluations. Nature Machine Intelligence, 2022, 4, 21-31.<\/td>Abstract<\/a><\/td><\/tr>
Lehtola S., Karttunen A. J., Free and open source software for computational chemistry education. WIREs Computational Molecular Science, 2022, 12, Article No e1610.<\/td>Abstract<\/a><\/td><\/tr>
L\u00f3pez-L\u00f3pez E., Fern\u00e1ndez-de Gortari E., Medina-Franco J. E., Yes SIR! On the structure\u2013inactivity relationships in drug discovery. Drug Discovery Today, 2022, 27, 2353-2362.<\/td>Abstract<\/a><\/td><\/tr>
Malavolta M., Pallante L., Mavkov B., Stojceski F., Grasso G., Korfiati A., Mavroudi S., Kalogeras A., Alexakos C., Martos V., Amoroso D., Di Benedetto G., Piga D., Theofilatos K., Deriu M. A., Survey on computational taste predictors. European Food Research and Technology, 2022, 248, 2215\u20132235.<\/td>Abstract<\/a><\/td><\/tr>
Medina-Franco J. L., Ch\u00e1vez-Hern\u00e1ndez A. L., L\u00f3pez-L\u00f3pez E., Sald\u00edvar-Gonz\u00e1lez F. I., Chemical multiverse: an expanded view of chemical space. Molecular Informatics, 2022, 41, 2200116.<\/td>Abstract<\/a><\/td><\/tr>
Medina\u2011Franco J. L., S\u00e1nchez\u2011Cruz N., L\u00f3pez\u2011L\u00f3pez E., D\u00edaz\u2011Eufracio B. I., Progress on open chemoinformatic tools for expanding and exploring the chemical space. Journal of Computer-Aided Molecular Design, 2022, 36, 341\u2013354.<\/td>Abstract<\/a><\/td><\/tr>
Musazade F., Jamalova N., Hasanov J., Review of techniques and models used in optical chemical structure recognition in images and scanned documents. Journal of Cheminformatics, 2022, 14, Article No 61.<\/td>Abstract<\/a><\/td><\/tr>
Polanski J., Unsupervised learning in drug design from self-organization to deep chemistry. International Journal of Molecular Sciences, 2022, 23, Article No 2797.<\/td>Abstract<\/a><\/td><\/tr>
Sald\u00edvar-Gonz\u00e1lez F. I., Aldas-Bulos V. D., Medina-Franco J. L., Plisson F., Natural product drug discovery in the artificial intelligence era. Chemical Science, 2022, 13, 1526\u20131546.<\/td>Abstract<\/a><\/td><\/tr>
Schwaller P., Vaucher A. C., Laplaza R., Bunne C., Krause A., Corminboeuf C., Laino T., Machine intelligence for chemical reaction space. WIREs Computational Molecular Science, 2022, 12, Article No e1604.<\/td>Abstract<\/a><\/td><\/tr>
Sheridan R. P., Stability of prediction in production ADMET models as a function of version: why and when predictions change. Journal of Chemical Information and Modeling, 2022, 62, 3477-3485.<\/td>Abstract<\/a><\/td><\/tr>
Tuvi-Arad I., Computational chemistry in the undergraduate classroom \u2013 pedagogical considerations and teaching challenges. Israel Journal of Chemistry, 2022, 62, Article No e202100042.<\/td>Abstract<\/a><\/td><\/tr>
Wang X., Bouzembrak Y., Lansink A. G. J. M. O., van der Fels-Klerx H. J., Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in Food Science and Food Safety, 2022, 21, 416\u2013434.<\/td>Abstract<\/a><\/td><\/tr>
Wigh D. S., Goodman J. M., Lapkin A. A., A review of molecular representation in the age of machine learning. WIREs Computational Molecular Science, 2022, 12, Article No e1603.<\/td>Abstract<\/a><\/td><\/tr>
Wu L., Wen Y., Leng D., Zhang Q., Dai C., Wang Z., Liu Z., Yan B., Zhang Y., Wang J., He S., Bo X., Machine learning methods, databases and tools for drug combination prediction. Briefings in Bioinformatics, 2022, 23, Article No bbab355.<\/td>Abstract<\/a><\/td><\/tr>
Wu Z., Jiang D., Wang J., Zhang X., Du H., Pan L., Hsieh C.-Y., Cao D., Hou T., Knowledge-based BERT: a method to extract molecular features like computational chemists. Briefings in Bioinformatics, 2022, 23, Article No bbac131.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/p>\n\n\n\n

2023<\/strong><\/p>\n\n\n\n

Castro Nascimento C. M., Silva Pimentel A., Do large language models understand chemistry? A conversation with ChatGPT. Journal of Chemical Information and Modeling, 2023, 63, 1649-1655.<\/td>Abstract<\/a><\/td><\/tr>
Cavasotto C. N., Di Filippo J. I., The impact of supervised learning methods in ultralarge high-throughput docking. Journal of Chemical Information and Modeling, 2023, 63, 2267\u20132280.<\/td>Abstract<\/a><\/td><\/tr>
Demir H., Daglar H., Gulbalkan H. C., Aksu G. O., Keskin S., Recent advances in computational modeling of MOFs: From molecular simulations to machine learning. Coordination Chemistry Reviews, 2023, 484, Article No 215112.<\/td>Abstract<\/a><\/td><\/tr>
Emenike M. E., Emenike B. U., Was this title generated by ChatGPT? Considerations for artificial intelligence text-generation software programs for chemists and chemistry educators. Journal of Chemical Education, 2023, 100, 1413-1418.<\/td>Abstract<\/a><\/td><\/tr>
Fathifar Z., Kalankesh L. R., Ostadrahimi A., Ferdousi R., New approaches in developing medicinal herbs databases. Database, 2023, Article No baac110.<\/td>Abstract<\/a><\/td><\/tr>
Goel M., Aggarwal R., Sridharan B., Pal P. K., Priyakumar U. D., Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods. WIREs Computational Molecular Science, 2023, 13, Article No e1637.<\/td>Abstract<\/a><\/td><\/tr>
Hagg A., Kirschner K. N., Open-source machine learning in computational chemistry. Journal of Chemical Information and Modeling, 2023, 63, 4505\u20134532.<\/td>Abst<\/a>ract<\/a><\/td><\/tr>
H\u00f6nig S. M. N., Lemmen C., Rarey M., Small molecule superposition: A comprehensive overview on pose scoring of the latest methods. WIREs Computational Molecular Science, 2023, 13, Article No e1640.<\/td>Abstract<\/a><\/td><\/tr>
Koutroumpa N.-M., Papavasileiou K. D., Papadiamantis A. G., Melagraki G., Afantitis A. A., Systematic review of deep learning methodologies used in the drug discovery process with emphasis on in vivo validation. International Journal of Molecular Sciences, 2023, 24, Article No 6573.<\/td>Abstract<\/a><\/td><\/tr>
Lisacek F., Tiemeyer M., Mazumder R., Aoki-Kinoshita K. F., Worldwide glycoscience informatics infrastructure: The GlySpace Alliance. JACS Au, 2023, 3, 4-12.<\/td>Abstract<\/a><\/td><\/tr>
Mercado R., Kearnes S. M., Coley C. W., Data sharing in chemistry: lessons learned and a case for mandating structured reaction data. Journal of Chemical Information and Modeling, 2023, 63, 4253-4265.<\/td>Abstract<\/a><\/td><\/tr>
Miranda-Salas J., Pe\u00f1a-Varas C., Valenzuela Mart\u00ednez I., Olmedo D. A., Zamora W. J., Ch\u00e1vez-Fumagalli M. A., Azevedo D. Q., Oliveira Castilho R., Maltarollo V. G., Ram\u00edrez D., Medina-Franco J. L., Trends and challenges in chemoinformatics research in Latin America. Artificial Intelligence in the Life Sciences, 2023, 3, Article No 100077.<\/td>Abstract<\/a><\/td><\/tr>
Ni Z., W\u00f6lk M., Jukes G., Mendivelso Espinosa K., Ahrends R., Aimo L., Alvarez-Jarreta J., Andrews S., Andrews R., Bridge A., Clair G. C., Conroy M. J., Fahy E., Gaud C., Goracci L., Hartler J., Hoffmann N., Kopczyinki D., Korf A., Lopez-Clavijo A. F., Malik A., Miranda Ackerman J., Molenaar M. R., O\u2019Donovan C., Pluskal T., , Shevchenko A., Slenter D., Siuzdak G., Kutmon M., Tsugawa H., Willighagen E. L., Xia J., O\u2019Donnell V. B., Fedorova M., Guiding the choice of informatics software and tools for lipidomics research applications. Nature Methods, 2023, 20, 193\u2013204.<\/td>Abstract<\/a><\/td><\/tr>
Ogawa K., Sakamoto D., Hosoki R., Computer science technology in natural products research: A review of its applications and implications. Chemical and Pharmaceutical Bulletin, 2023, 71, 486\u2013494.<\/td>Abstract<\/a><\/td><\/tr>
Pal R., Chattaraj P. K., Electrophilicity index revisited. Journal of Computational Chemistry, 2023, 44, 278\u2013297.<\/td>Abstract<\/a><\/td><\/tr>
Rojas C., Ballabio D., Consonni V., Su\u00e1rez-Estrella D., Todeschini R., Classification-based machine learning approaches to predict the taste of molecules: a review. Food Research International, 2023, 171, Article No 113036.<\/td>Abstract<\/a><\/td><\/tr>
Sampaio P. S., Fernandes P., Machine Learning: a suitable method for biocatalysis. Catalysts, 2023, 13, Article No 961.<\/td>Abstract<\/a><\/td><\/tr>
Simoben C. V., Babiaka S. B., Moumbock A. F. A., Namba-Nzanguim C. T., Eni D. B., Medina-Franco J. L., G\u00fcnther S., Ntie-Kang F., Sippl W., Challenges in natural product-based drug discovery assisted with in silico-based methods. RSC Advances, 2023, 13, Article No 31578.<\/td>Abstract<\/a><\/td><\/tr>
Silva-Mendon\u00e7a S., de Sousa Vit\u00f3ria A. R., Woerle de Lima T., Galv\u00e3o-Filho A. R., Horta Andrade C., Exploring new horizons: Empowering computer-assisted drug design with few-shot learning. Artificial Intelligence in the Life Sciences, 2023, 4, Article No 100086.<\/td>Abstract<\/a><\/td><\/tr>
Sohraby F., Nunes-Alves A., Advances in computational methods for ligand binding kinetics. Trends in Biochemical Sciences, 2023, 48, 437-449.<\/td>Abstract<\/a><\/td><\/tr>
Tran T. T. V., Wibowo A. S., Tayara H., Chong K. T., Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. Journal of Chemical Information and Modeling, 63, 2023, 2628\u20132643.<\/td>A<\/a>bstract<\/a><\/td><\/tr>
Tran-Nguyen V.-K., Ballester P. J., Beware of simple methods for structure-based virtual screening: the critical importance of broader comparisons. Journal of Chemical Information and Modeling, 2023, 63, 1401-1405.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2024<\/strong><\/p>\n\n\n\n

\n
\n
Avellaneda-Tamayo J. F., S\u00e1nchez-Ruiz A., Savic B., Medina-Franco J. L., Colmenarejo G., Quimioinform\u00e1tica, inteligencia artificial y la qu\u00edmica de alimentos. TIP Revista Especializada en Ciencias Qu\u00edmico-Biol\u00f3gicas, 2024, 27, 1-17.<\/td>Abstract<\/a><\/td><\/tr>
Bajorath J., Chemical language models for molecular design. Molecular Informatics, 2024, 43, Article No e202300288.<\/td>Ab<\/a>stract<\/a><\/td><\/tr>
Iqbal A. B., Shah I. A., Injila, Assad A., Ahmed M., Shah S. Z., A review of deep learning algorithms for modeling drug interactions. Multimedia Systems, 2024, 30, Article No 124.<\/td>Abstract<\/a><\/td><\/tr>
Kirtania D. K., ChatGPT generated content and similarity index in chemistry. Journal of Chemical Information and Modeling, 2024, 64, 2132\u20132135.<\/td>Abstract<\/a><\/td><\/tr>
L\u00f3pez-P\u00e9rez K., Avellaneda-Tamayo J. F., Chen L., Edgar L\u00f3pez-L\u00f3pez E., Ju\u00e1rez-Mercado K. E., Medina-Franco J. L., Miranda-Quintana R. A., Molecular similarity: Theory, applications, and perspectives. Artificial Intelligence Chemistry, 2024, 2, Article No 100077.<\/td>Abstract<\/a><\/td><\/tr>
Martinez-Mayorga K., Rosas-Jim\u00e9nez J. G., Gonzalez-Ponce K., L\u00f3pez-L\u00f3pez E., Neme A., Medina-Franco J. L., The pursuit of accurate predictive models of the bioactivity of small molecules. Chemical Science, 2024, 15, 1938-1952.<\/td>Abstract<\/a><\/td><\/tr>
McGibbon M., Shave S., Dong J., Gao Y., Houston D. R., Xie J., Yang Y., Schwaller P., Blay V., From intuition to AI: evolution of small molecule representations in drug discovery. Briefings in Bioinformatics, 2024, Article No bbad422.<\/td>Abstract<\/a><\/td><\/tr>
Minkiewicz P., Iwaniak A., Darewicz M., Contemporary bioinformatics and cheminformatics support for food peptidomics. Current Opinion in Food Science, 2024, 56, Article No 101125.<\/td>Abstract<\/a><\/td><\/tr>
Nguyen-Vo T.-H., Teesdale-Spittle P., Harvey J. E., Nguyen B. P., Molecular representations in bio-cheminformatics. Memetic Computing, 2024, 16, 519\u2013536.<\/td>Abstract<\/a><\/td><\/tr>
Quadros de Azevedo D., Mattos Campioni B., Lima F. A. P., Medina-Franco J. L., Oliveira Castilho R., Gon\u00e7alves Maltarollo V., A critical assessment of bioactive compounds databases. Future Medicinal Chemistry, 2024, 16, 1029-1051.<\/td>Abstract<\/a><\/td><\/tr>
Voinarovska V., Kabeshov M., Dudenko D., Genheden S., Tetko I. V., When yield prediction does not yield prediction: an overview of the current challenges. Journal of Chemical Information and Modeling, 2024, 64, 42\u201356.<\/td>Abs<\/a>tract<\/a><\/td><\/tr>
Wang L., Lu Y., Li D., Zhou Y., Yu L., Eguiagaray I. M., Campbell H., Li X., Theodoratou E., The landscape of the methodology in drug repurposing using human genomic data: a systematic review. Briefings in Bioinformatics, 2024, 25, Article No bbad527.<\/td>Abstract<\/a><\/td><\/tr>
Zhang Y., Bao X., Zhu Y., Dai Z., Shen Q., Xue Y., Advances in machine learning screening of food bioactive compounds. Trends in Food Science & Technology, 2024, 150, Article No 045782024.<\/td>Abstract<\/a><\/td><\/tr>
Zhang Y., Deng Z., Xu X., Feng Y., Junliang S., Application of artificial intelligence in drug\u2013drug interactions prediction: a review. Journal of Chemical Information and Modeling, 64, 2158\u20132173.<\/td>Abstract<\/a><\/td><\/tr>
Zhao Y., Yin J., Zhang L., Zhang Y., Chen X., Drug\u2013drug interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics, 2024, 25, Article No bbad445.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n<\/div>\n\n\n\n

2025<\/strong><\/p>\n\n\n\n

Chetry A. B., Ohto K., From molecules to data: the emerging impact of chemoinformatics in chemistry. Journal of Cheminformatics, 17, Article No 121.<\/td>Abstract<\/a><\/td><\/tr>
Cui Z., Qi C., Zhou T., Yu Y., Wang Y., Zhang Z., Zhang Y., Wang W., Liu W., Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development. Comprehensive Reviews in Food Science and Food Safety, 24, Article No e70068<\/td>Abstract<\/a><\/td><\/tr>
Davies S. R., Working in biocuration: contemporary experiences and perspectives. Database, 2025, Article No baaf003.<\/td>Abstract<\/a><\/td><\/tr>
Gangwal A., Lavecchia A., AI-Driven drug discovery for rare diseases. Journal of Chemical Information and Modeling, 2025, 65, 2214\u20132231.<\/td>Abstract<\/a><\/td><\/tr>
Khater T., Alkhatib S. A., AlShehhi A., Pitsalidis C., Pappa A. M., Ngo S. T., Chan V., Truong V. K., Generative artificial intelligence based models optimization towards molecule design enhancement. Journal of Cheminformatics, 2025, 17, Article No 116.<\/td>Abstract<\/a><\/td><\/tr>
Ko\u00e7ak M., Ak\u00e7al\u0131 Z., The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023. Journal of Cheminformatics, 2025, 17, Article No 71.<\/td>Abstract<\/a><\/td><\/tr>
Marrano A., Cabugos L., Hafner A., Kapoor B., McNamara J., O\u2019Donnell M., Reiser L., Tello-Ruiz M. K., Zhang H., Staton M., A teaching and training framework to promote findable, accessible, interoperable, and reusable data generation in agriculture. Database, 2025, Article No baaf034.<\/td>Abstract<\/a><\/td><\/tr>
Michels J., Bandarupalli R., Akbari A. A., Le T., Xiao H., Li J., Hom E. F. Y., Natural language processing methods for the study of protein\u2212ligand interactions. Journal of Chemical Information and Modeling, 2025, 65, 2191\u22122213.<\/td>Abstract<\/a><\/td><\/tr>
Sch\u00fcr C., Schirmer K., Baity-Jesi M., On the comparability between studies in predictive ecotoxicology. Computational Toxicology, 2025, 35, Article No 100367.<\/td>Abstract<\/a><\/td><\/tr>
Steinbeck C., The evolution of open science in cheminformatics: a journey from closed systems to collaborative innovation. Journal of Cheminformatics, 2025, 17, Article No 44.<\/td>Abstract<\/a><\/td><\/tr>
Ver\u00edssimo G. C., Ferreira R. S., Maltarollo V. G., Ultra-large virtual screening: definition, recent advances, and challenges in drug design. Molecular Informatics, 2025, 44, Article No e202400305.<\/td>Abstract<\/a><\/td><\/tr>
Williams A. J., Richard A. M., Three pillars for ensuring public access and integrity of chemical databases powering cheminformatics. Journal of Cheminformatics, 2025, 17, Article No 40.<\/td>Abstract<\/a><\/td><\/tr>
Zhou M., Lima J. C. R., Zhao H., Zhang J., Xu C., Santos-J\u00fanior C. D., Wu H., Harnessing AI for enhanced screening of antimicrobial bioactive compounds in food safety and preservation. Trends in Food Science & Technology, 2025, 157, Article No 104887.<\/td>Abstract<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/p>\n\n\n\n

2026<\/strong><\/p>\n\n\n\n

Erckes V., Abderrahmane M., Jusot M., Steuer C., Ochoa R., Peptide cheminformatics tools: making computational tasks accessible in peptide drug discovery, Drug Discovery Today, 2026, 31, Article No 104612.<\/td>Abstract<\/a><\/td><\/tr>
Ertl P., Molecules in Wikipedia: analysis of their chemical diversity, functional roles, and popularity. J. Chem. Inf. Model., 2026, 66, 387\u2013394.<\/td>Abstract<\/a><\/td><\/tr>
Esaki T., Ikeda K., Data curation in cheminformatics: importance and implementation. Journal of Cheminformatics, 2026, 18, Article No 43.<\/td>Abstract<\/a><\/td><\/tr>
L\u00f3pez-L\u00f3pez E., S\u00e1nchez J. P., Martinez-Cort\u00e9s M. S., de la Fuente-Nunez C., Medina-Franco J.-L., 2026, Exploring and expanding the chemical multiverse of peptides. Chemical Science, 2026, 17, 1461-1479.<\/td>Abstract<\/a><\/td><\/tr>
Malli A., Vasyutyn D., Kim J. R., Advances in Machine Learning Models for predicting enzyme kinetic parameters. Journal of Chemical Information and Modeling, 2026, 66, 42\u201360.<\/td>Abstract<\/a><\/td><\/tr>
<\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Reviews 2006 Maldonado A. G., Doucet J. P., Petitjean M., Fan B.-Y., Molecular similarity and diversity in chemoinformatics: From theory to applications. Molecular Diversity, 2006, 10, 39-79. Abstract 2007 Degtyarenko K., Ennis M., Garavelli J. S., Good annotation practice for chemical data in biology. In Silico Biology, 2007, 7 (Supplement 2), 45-56. Abstract Scior T.,…<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_lmt_disableupdate":"no","_lmt_disable":"no","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"left","_kad_post_sidebar_id":"sidebar-primary","_kad_post_content_style":"boxed","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"class_list":["post-171","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/pages\/171","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/comments?post=171"}],"version-history":[{"count":112,"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/pages\/171\/revisions"}],"predecessor-version":[{"id":1087,"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/pages\/171\/revisions\/1087"}],"wp:attachment":[{"href":"https:\/\/biochemia.uwm.edu.pl\/metachemibio\/wp-json\/wp\/v2\/media?parent=171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}