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., 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.	Abstract

2008

Aoki-Kinoshita K. F., An introduction to bioinformatics for glycomics research. PLoS Computational Biology, 2008, 4, Article No e1000075.

Abstract

2009

Martínez-Mayorga K., Medina-Franco J. L., Chapter 2. Chemoinformatics – applications in food chemistry. Advances in Food and Nutrition Research, 2009, 58, 33-56.

Abstract

2010

Ertl P., Molecular structure input on the web. Journal of Cheminformatics, 2010, 2, Article No 1.	Abstract
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, 1189–1204.	Abstract
Judson R., Public databases supporting computational toxicology. Journal of Toxicology and Environmental Health B: Critical Reviews, 2010, 13, 218-231.	Abstract

2011

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.	Abstract
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, 2554–2574.	Abstract
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.	Abstract
O’Boyle 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.	Abstract
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, 4331–4348.	Abstract
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.	Abstract
Varnek A., Baskin I. I., Chemoinformatics as a theoretical chemistry discipline. Molecular Informatics, 2011, 30, 20–32.	Abstract
Vazquez M., Krallinger M., Leitner F., Valencia A., Text mining for drugs and chemical compounds: methods, tools and applications. Molecular Informatics, 2011, 30, 506–519.	Abstract
Williams A. J., Ekins S., A quality alert and call for improved curation of public chemistry databases. Drug Discovery Today, 2011, 16, 747–750.	Abstract

2012

Malkaram S. A., Hassan Y. I., Zempleni J., Online tools for bioinformatics analyses in nutrition sciences. Advances in Nutrition, 2012, 3, 654-665.	Abstract
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.	Abstract
Reymond J.-L., Awale M., Exploring chemical space for drug discovery using the chemical universe database. ACS Chemical Neuroscience, 2012, 3, 649–657.	Abstract
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.	Abstract
Wishart D. S., Chapter 3: Small Molecules and Disease. PLoS Computational Biology, 2012, 8, Article No e1002805.	Abstract

2013

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.	Abstract
Chagoyen M., Pazos F., Tools for the functional interpretation of metabolomic experiments. Briefings in Bioinformatics. 2013, 14, 737-744.	Abstract
Chen B., Wild D. J., 2013, Practice and challenges of building a semantic framework for chemogenomics research. Molecular Informatics, 2013, 32, 1000–1008.	Abstract
Clark A. M., Williams A. J., Ekins S., Cheminformatics workflows using mobile apps. Chem-Bio Informatics Journal, 2013, 13, 1-18.	Abstract
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.	Abstract
Gómez-Pérez A., Martínez-Romero M., Rodríguez-González A., Vázquez G., Vázquez-Naya J. M., Ontologies in medicinal chemistry: current status and future challenges. Current Topics in Medicinal Chemistry, 2013, 13, 576-590.	Abstract
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.	Abstract
Hartler J., Tharakan R., Köfeler 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.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
Libman D., Huang L., Chemistry on the go: Review of chemistry apps on smartphones. Journal of Chemical Education, 2013, 90, 320–325.	Abstract
Medina-Franco J. L., Advances in computational approaches for drug discovery based on natural products. Revista Latinoamericana de Quimica, 2013, 41, 95-110.	Abstract
Minkiewicz P., Miciński 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.	Abstract
Scheubert K., Hufsky F., Böcker S., 2013, Computational mass spectrometry for small molecules. Journal of Cheminformatics, 2013, 5, Article No 12.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract

2014

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.	Abstract
Campbell M. P., Ranzinger R., Lütteke 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.	Abstract
Eltyeb S., Salim N., Chemical named entities recognition: a review on approaches and applications. Journal of Cheminformatics, 2014, 6, Article No 17.	Abstract
Fearnley L. G., Davis M. J., Ragan M. A., Nielsen L. K., Extracting reaction networks from databases – opening Pandora’s box. Briefings in Bioinformatics, 2014, 15, 973-983.	Abstract
Gasteiger J., Solved and unsolved problems of chemoinformatics. Molecular Informatics, 2014, 33, 454-457.	Abstract
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, 2627–2635.	Abstract
Martínez-Mayorga K., Medina-Franco J. L. (Editors), Foodinformatics. Applications of chemical information to food chemistry. Springer International Publishing AG, Cham, Switzerland, 2014.	Abstracts
McDonald A. G., Tipton K. F., Fifty-five years of enzyme classification: advances and difficulties. FEBS Journal, 2014, 281, 583–592.	Abstract
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.	Abstract
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.	Abstract
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, 20–25.	Abstract
Willett P., The calculation of molecular structural similarity: principles and practice. Molecular Informatics, 2014, 33, 403–413.	Abstract

2015

Audouze K., Taboureau O., Chemical biology databases: from aggregation, curation to representation. Drug Discovery Today: Technology, 2015, 14, 25-29.	Abstract
Bawden D., Storing the wisdom: chemical concepts and chemoinformatics. Informatics, 2015, 2, 50-67.	Abstract
Bolton E., Reporting biological assay screening results for maximum impact. Drug Discovery Today: Technology, 2015, 14, 31-36.	Abstract
Cereto-Massagué A., Ojeda M. J., Valls C., Mulero M., Garcia-Vallvé S., Pujadas G., Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71, 58–63.	Abstract
Cereto-Massagué A., Ojeda M. J., Valls C., Mulero M., Pujadas G., Garcia-Vallve S., Tools for in silico target fishing. Methods, 2015, 71, 98–103.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
Kos A., Himmler H.-J., Efficient Internet searches for chemists. Chemical Informatics, 2015, 1, Article No 12.	Abstract
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, 2068–2076.	Abstract
Reymond J.-L., The chemical space project. Accounts of Chemical Research, 2015, 48, 722–730.	Abstract
Richter L., Ecker G. F., Medicinal chemistry in the era of big data. Drug. Discovery Today: Technologies, 2015, 14, 37–41.	Abstract
Southan C., 2015, Expanding opportunities for mining bioactive chemistry from patents. Drug Discovery Today: Technologies, 2015, 14, 3-9.	Abstract
Warr W. A., Many InChIs and quite some feat. Journal of Computer Aided Molecular Design, 2015, 29, 681–694.	Abstract

2016

Chen X., Yan C. C., Zhang X., Zhang X., Dai F., Yin J., Zhang Y., Drug–target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics, 2016, 17, 696-712.	Abstract
Dimova D., Bajorath J., Advances in activity cliff research. Molecular Informatics, 2016, 35, 181–191.	Abstract
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, 1243–1252.	Abstract
Gameiro D., Pérez-Pérez M., Pérez-Rodríguez G., Monteiro G., Azevedo N. F., Lourenço A., Computational resources and strategies to construct single-molecule metabolic models of microbial cells. Briefings in Bioinformatics, 2016, 17, 863-876.	Abstract
Gasteiger J., Chemoinformatics: achievements and challenges, a personal view. Molecules, 2016, 21, Article No 151.	Abstract
Glaab E., Building a virtual ligand screening pipeline using free software: a survey. Briefings in Bioinformatics, 2016, 17, 352-366.	Abstract
Lavecchia A., Cerchia C., In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discovery Today, 2016, 21, 288-298.	Abstract
Lewis R., Deheuvels J., Ertl P., Pirard B., Sirockin F., Building compound archives for the future. Molecular Informatics, 2016, 35, 580–582.	Abstract
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.	Abstract
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.	Abstract
Misra B. P., van der Hooft J. J. J., Updates in metabolomics tools and resources: 2014–2015. Electrophoresis, 2016, 37, 86-110.	Abstract
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.	Abstract
Nongonierma A. B., FitzGerald R. J., Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review. RSC Advances, 2016, 6, 75400–75413.	Abstract
Pehrsson P. R., Haytowitz D. B., Food composition databases. in: Caballero B., Finglas P. M., Toldrá F. (Editors), Encyclopedia of Food and Health, Elsevier Ltd, 2016, pp 16-21.	Abstract
Przybyła 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.	Abstract
Puchades-Carrasco L., Palomino-Schätzlein M., Pérez-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.	Abstract
Raies A. B., Bajic V. B., In silico toxicology: computational methods for the prediction of chemical toxicity. WIREs Computational Molecular Science, 2016, 6, 147–172.	Abstract
Senthilkumar B., Rajasekaran R., Computational resources for designing peptide based drugs preferred in the field of nanomedicine. Journal of Bionanoscience, 2016, 10, 1-14.	Abstract
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, 615–621.	Abstract
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, 23–35.	Abstract

2017

Abriata L. A., Web apps come of age for molecular sciences. Informatics, 2017, 4, Article No 28.	Abstract
Awale M., Visini R., Probst D., Arús-Pous J., Reymond J.-L., 2017, Chemical space: Big data challenge for molecular diversity. Chimia, 71, 661-666.	Abstract
Böcker S., Searching molecular structure databases using tandem MS data: are we there yet? Current Opinion in Chemical Biology, 2017, 36, 1-6.	Abstract
Boezio B., Audouze K., Ducrot P., Taboureau O., Network-based approaches in pharmacology. Molecular Informatics, 2017, 36, Article No 1700048.	Abstract
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.	Abstract
De Vivo M., Cavalli A., Recent advances in dynamic docking for drug discovery. WIREs Computational Molecular Sciences, 2017, 7, Article No e1320.	Abstract
Frainay C., Jourdan F., Computational methods to identify metabolic sub-networks based on metabolomic profiles. Briefings in Bioinformatics, 2017, 18, 43-56.	Abstract
Goldmann D., Zdrazil B., Digles D., Ecker G. F., Empowering pharmacoinformatics by linked life science data. Journal of Computer Aided Molecular Design, 2017, 31, 319–328.	Abstract
González-Medina M., Naveja J. J., Sánchez-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.	Abstract
Haug K., Salek R. M., Steinbeck C., Global open data management in metabolomics. Current Opinion in Chemical Biology, 2017, 36, 58–63.	Abstract
Hawkins P. C. D., Conformation generation: the state of the art. Journal of Chemical Information and Modeling, 2017, 57, 1747–1756.	Abstract
Houk K. N., Liu F., Holy grails for computational organic chemistry and biochemistry. Accounts of Chemical Research, 2017, 50, 539-543.	Abstract
Kim S. M., Peña 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.	Abstract
Krallinger M., Rabal O., Lourenço A., Oyarzabal J., Valencia A., Information retrieval and text mining technologies for chemistry. Chemical Reviews, 2017, 117, 7673-7761.	Abstract
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.	Abstract
Minkiewicz P., Iwaniak A., Darewicz M., Annotation of peptide structures using SMILES and other chemical codes–practical solutions. Molecules, 2017, 22, Article No 2075.	Abstract
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.	Abstract
Schymanski E. L., Ruttkies C., Krauss M., Brouard C., Kind T., Dührkop K., Allen F., Vaniya V., Verdegem D., Böcker S., Rousu J., Shen H., Tsugawa H., Sajed T., Fiehn F., Ghesquière B., Neumann S., Critical assessment of small molecule identification 2016: automated methods. Journal of Cheminformatics, 2017, 9, Article No 22.	Abstract
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.	Abstract
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.	Abstract
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, 64–69.	Abstract
Williams A. J., Pence H. E., The future of chemical information is now. Chemistry International, 2017, 39 (3), 9-14.	Abstract
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.	Abstract

2018

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.	Abstract
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, 57–64.	Abstract
Blaženović 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.	Abstract
Brown A. S., Patel C. J., A review of validation strategies for computational drug repositioning. Briefings in Bioinformatics, 19, 2018, 174–177.	Abstract
Chen H., Kogej T., Engkvist O., Cheminformatics in drug discovery, an industrial perspective. Molecular Informatics, 2018, 37, Article No 1800041.	Abstract
Connolly Martin Y., How medicinal chemists learned about log P. Journal of Computer-Aided Molecular Design, 2018, 32, 809–819.	Abstract
Do P.-C., Lee E. H., Le L., Steered molecular dynamics simulation in rational drug design. Journal of Chemical Information and Modeling, 2018, 58, 1473–1482.	Abstract
Engel T., Gasteiger J. (editors), Applied chemoinformatics: achievements and future opportunities, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2018	Abstracts
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, 225–233.	Abstract
Grimme S., Schreiner P. R., Computational chemistry: the fate of current methods and future challenges. Angewandte Chemie International Edition, 2018, 57, 4170-4176.	Abstract
Hessler G., Baringhaus K.-H., Artificial intelligence in drug design. Molecules, 2018, 23, Article No 2520.	Abstract
Katiyar R. S., Jha P. K., Molecular simulations in drug delivery: Opportunities and challenges. WIREs Computational Molecular Sciences. 2018, 8, Article No e1358.	Abstract
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, 506–523.	Abstract
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.	Abstract
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.	Abstract
Prieto-Martínez F. D., Arciniega M., Medina-Franco J. L., Molecular docking: current advances and challenges. TIP Revista Especializada en Ciencias Químico-Biológicas, 2018, 21 (Supl. 1), 65-87.	Abstract
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.	Abstract
Scotti L., Júnior 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-Júnior 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.	Abstract
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, 878–892.	Abstract
Sotriffer C., Docking of covalent ligands: challenges and approaches. Molecular Informatics, 2018, 37, Article No 1800062.	Abstract
Tsugawa H., Advances in computational metabolomics and databases deepen the understanding of metabolisms. Current Opinion in Biotechnology 2018, 54, 10–17.	Abstract
Vilar S., Friedman C., Hripcsak G., Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media. Briefings in Bioinformatics, 2018, 19, 863–877.	Abstract
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, 87–92.	Abstract
Winkler D. A., Sparse QSAR modelling methods for therapeutic and regenerative medicine. Journal of Computer-Aided Molecular Design, 2018, 32, 497–509.	Abstract

2019

Athar M., Sona A. N., Bekono B. D., Ntie-Kang F., Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”. Physical Sciences Reviews, 2019, 4, Article No 20180101.	Abstract
Awale M., Reymond J.-L., Web-Based tools for polypharmacology prediction. Methods in Molecular Biology, 2019, 1888, 255-272.	Abstract
Blanco-Míguez A., Fdez-Riverola F., Sánchez B., Lourenço A., Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Briefings in Bioinformatics, 2019, 21, 1032-1056.	Abstract
Byrne R., Schneider G., In silico target prediction for small molecules. Methods in Molecular Biology, 2019, 1888, 273-309.	Abstract
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.	Abstract
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, 17–24.	Abstract
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, 119–128.	Abstract
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	Abstract
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.	Abstract
Mater A. C.,  Coote M. L., Deep learning in chemistry. Journal of Chemical Information and Modeling, 2019, 59, 2545-2559.	Abstract
Minkiewicz P., Turło M., Iwaniak A., Darewicz M., Free accessible databases as a source of information about food components and other compounds with anticancer activity–brief review. Molecules, 2019, 24, Article No 789.	Abstract
Muñoz E., Nováček V., Vandenbussche P.-Y., Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models. Briefings in Bioinformatics, 2019, 20, 190–202.	Abstract
Pérez-Sianes J., Pérez-Sánchez H., Díaz F., Virtual screening meets deep learning. Current Computer-Aided Drug Design, 2019, 15, 6-28	Abstract
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.	Abstract
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.	Abstract
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, 947–961.	Abstract
Shen C., Ding J., Wang Z., Cao D., Ding X., Hou T., From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WIREs Computational Molecular Science. 2019, Article No e1429.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
Tuvi-Arad I., Blonder R., Technology in the service of pedagogy: Teaching with chemistry databases. Israel Journal of Chemistry, 2019, 59, 572-582.	Abstract

2020

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.	Abstract
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.	Abstract
Chen Y., Kirchmair J., Cheminformatics in natural product-based drug discovery. Molecular Informatics, 2020, 39, Article No 2000171.	Abstract
David L., Thakkar A., Mercado R., Engkvist O., Molecular representations in AI‑driven drug discovery: a review and practical guide. Journal of Cheminformatics, 2020, 12, Article No 56.	Abstract
Hemmerich J., Ecker G. F., In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WIREs Computational Molecular Sciences, 2020, 10, Article No e1475.	Abstract
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.	Abstract
Journal of Chemical Education, special issue concerning chemistry teaching during Covid-19 pandemia	Abstracts
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.	Abstract
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.	Abstract
Medina-Franco J. L., Saldívar-González F. I., Cheminformatics to characterize pharmacologically active natural products. Biomolecules, 2020, 10, Article No 1566	Abstract
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.	Abstract
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.	Abstract
Ntie-Kang F., Svozil D., An enumeration of natural products from microbial, marine and terrestrial sources. Physical Sciences Reviews, 2020, 5, Article No 20180121.	Abstract
Paananen J., Fortino V., An omics perspective on drug target discovery platforms. Briefings in Bioinformatics, 2020, 21, 1937–1953.	Abstract
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.	Abstract
Rodrigues T., Bernardes G. J. L., Machine learning for target discovery in drug development. Current Opinion in Chemical Biology, 2020, 56, 16–22.	Abstract
Saldívar‑González F. I., Huerta‑García C. S., Medina-Franco J. L., Chemoinformatics‑based enumeration of chemical libraries: a tutorial. Journal of Cheminformatics, 2020, 12, Article No 64.	Abstract
Saldívar-González F. I., Medina-Franco J. L., Chapter 3 – Chemoinformatics approaches to assess chemical diversity and complexity of small molecules. in “Small Molecule Drug Discovery, Methods, Molecules and Applications”, Ed. by Trabocchi A., Lenci E., Elsevier Inc., 2020, pp 83-102.	Abstract
Schaller D., Šribar 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.	Abstract
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.	Abstract
Sorokina M., Steinbeck C., Review on natural products databases: where to find data in 2020. Journal of Cheminformatics, 2020, 12, Article No 20.	Abstract
Stumpfe D., Bajorath J., Current trends, overlooked Issues, and unmet challenges in virtual screening. Journal of Chemical Information and Modeling, 2020, 60, 4112–4115	Abstract
Tabei Y., Scalable prediction of compound-protein interaction on compressed molecular fingerprints. Molecular Informatics, 2020, 39, Article No 1900130.	Abstract
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
Tantillo D. J., Interrogating chemical mechanisms in natural products biosynthesis using quantum chemical calculations. WIREs Computational Molecular Science, 2020, 10, Article No e1453.	Abstract
Wang C., Kurgan L., Survey of similarity-based prediction of drug-protein interactions. Current Medicinal Chemistry, 2020, 27, 5856-5886.	Abstract
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, 63–70.	Abstract

2021

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.	Abstract
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.	Abstract
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, 1425–1438.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract
Jorner K., Tomberg A., Bauer C., Sköld C., Norrby P.-O., Organic reactivity from mechanism to machine learning. Nature Reviews Chemistry, 2021, 5, 240–255.	Abstract
López-López E., Bajorath J., Medina-Franco J. L., Informatics for chemistry, biology, and biomedical sciences. Journal of Chemical Information and Modeling, 2021, 61, 26-35.	Abstract
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, 1604–1619.	Abstract
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.	Abstract
Medina-Franco J. L., Martinez-Mayorga K., Fernández-de Gortari E., Kirchmair J., Bajorath J., Rationality over fashion and hype in drug design. F1000Research, 2021, 10, Article No 397	Abstract
Molga K., Szymkuć S., Grzybowski B. A., Chemist ex machina: advanced synthesis planning by computers. Accounts of Chemical Research, 2021, 54, 1094-1106.	Abstract
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.	Abstract
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.	Abstract
Patil V. M., Masand N., Natural product databases and tools for anti-cancer drug discovery. Mini-Reviews in Medicinal Chemistry, 2021, 21, 2772-2785.	Abstract
Pérez Santín E., Rodríguez Solana R., González García M., García Suárez M. D. M., Blanco Díaz G. D., Cima Cabal M. D., Moreno Rojas J. M., López Sánchez J. I., Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIREs Computational Molecular Sciences., 2021, 11, Article No e1516.	Abstract
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, 1790–1818.	Abstract
Tanoli Z, Seemab U., Scherer A., Wennerberg W., Tang J., Vähä-Koskela M., Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Briefings in Bioinformatics, 2021, 22, 1656–1678.	Abstract
Terayama K., Sumita M., Tamura R., Tsuda K., Black-box optimization for automated discovery. Accounts of Chemical Research, 2021, 54, 1334–1346.	Abstract
Tomasella C., Floris M., Guccione S., Pappalardo M., Basile L., Peptidomimetics in silico. Molecular Informatics, 2021, 40, Article No 2000087.	Abstract
Walters W. P., Barzilay R., Applications of deep learning in molecule generation and molecular property prediction. Accounts of Chemical Research, 2021, 54, 263–270.	Abstract
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.	Abstract
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.	Abstract
Zhao S., Su C., Lu Z., Wang F., Recent advances in biomedical literature mining. Briefings in Bioinformatics, 2021, 22, article No bbaa057.	Abstract

2022

Abramov Y. A., Sun G., Zeng Q., Emerging landscape of computational modeling in pharmaceutical development. Journal of Chemical Information and Modeling, 2022, 62, 1160–1171.	Abstract
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.	Abstract
Bojar D., Lisacek F., Glycoinformatics in the artificial intelligence era. Chemical Reviews, 2022, 122, 15971–15988.	Abstract
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.	Abstract
Dhakal A., McKay C., Tanner T. J., Cheng J., Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions. Briefings in Bioinformatics, 2022, 23, Article No bbab476.	Abstract
Du B.-X., Qin Y., Jiang Y.-F., Xu Y., Yiu S.-M., Yu H., Shi J.-Y., Compound–protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discovery Today, 2022, 27, 1350-1366.	Abstract
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.	Abstract
Fernández-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.	Abstract
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, 1863–1872.	Abstract
Goel M., Bagler G., Computational gastronomy: A data science approach to food. Journal of Bioscience, 2022, 47, Article No 12.	Abstract
Gonzalez-Hernandez G., Krallinger M., Muñoz M., Rodriguez-Esteban R., Uzuner Ö., Hirschman L., Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers. Database, 2022, Article No baac071.	Abstract
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.	Abstract
Lehtola S., Karttunen A. J., Free and open source software for computational chemistry education. WIREs Computational Molecular Science, 2022, 12, Article No e1610.	Abstract
López-López E., Fernández-de Gortari E., Medina-Franco J. E., Yes SIR! On the structure–inactivity relationships in drug discovery. Drug Discovery Today, 2022, 27, 2353-2362.	Abstract
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, 2215–2235.	Abstract
Medina-Franco J. L., Chávez-Hernández A. L., López-López E., Saldívar-González F. I., Chemical multiverse: an expanded view of chemical space. Molecular Informatics, 2022, 41, 2200116.	Abstract
Medina‑Franco J. L., Sánchez‑Cruz N., López‑López E., Díaz‑Eufracio B. I., Progress on open chemoinformatic tools for expanding and exploring the chemical space. Journal of Computer-Aided Molecular Design, 2022, 36, 341–354.	Abstract
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.	Abstract
Polanski J., Unsupervised learning in drug design from self-organization to deep chemistry. International Journal of Molecular Sciences, 2022, 23, Article No 2797.	Abstract
Saldívar-González 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, 1526–1546.	Abstract
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.	Abstract
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.	Abstract
Tuvi-Arad I., Computational chemistry in the undergraduate classroom – pedagogical considerations and teaching challenges. Israel Journal of Chemistry, 2022, 62, Article No e202100042.	Abstract
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, 416–434.	Abstract
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.	Abstract
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.	Abstract
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.	Abstract

2023

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.	Abstract
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, 2267–2280.	Abstract
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.	Abstract
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.	Abstract
Fathifar Z., Kalankesh L. R., Ostadrahimi A., Ferdousi R., New approaches in developing medicinal herbs databases. Database, 2023, Article No baac110.	Abstract
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.	Abstract
Hagg A., Kirschner K. N., Open-source machine learning in computational chemistry. Journal of Chemical Information and Modeling, 2023, 63, 4505–4532.	Abstract
Hönig 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.	Abstract
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.	Abstract
Lisacek F., Tiemeyer M., Mazumder R., Aoki-Kinoshita K. F., Worldwide glycoscience informatics infrastructure: The GlySpace Alliance. JACS Au, 2023, 3, 4-12.	Abstract
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.	Abstract
Miranda-Salas J., Peña-Varas C., Valenzuela Martínez I., Olmedo D. A., Zamora W. J., Chávez-Fumagalli M. A., Azevedo D. Q., Oliveira Castilho R., Maltarollo V. G., Ramírez 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.	Abstract
Ni Z., Wölk 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’Donovan C., Pluskal T., , Shevchenko A., Slenter D., Siuzdak G., Kutmon M., Tsugawa H., Willighagen E. L., Xia J., O’Donnell V. B., Fedorova M., Guiding the choice of informatics software and tools for lipidomics research applications. Nature Methods, 2023, 20, 193–204.	Abstract
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, 486–494.	Abstract
Pal R., Chattaraj P. K., Electrophilicity index revisited. Journal of Computational Chemistry, 2023, 44, 278–297.	Abstract
Rojas C., Ballabio D., Consonni V., Suárez-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.	Abstract
Sampaio P. S., Fernandes P., Machine Learning: a suitable method for biocatalysis. Catalysts, 2023, 13, Article No 961.	Abstract
Simoben C. V., Babiaka S. B., Moumbock A. F. A., Namba-Nzanguim C. T., Eni D. B., Medina-Franco J. L., Günther 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.	Abstract
Silva-Mendonça S., de Sousa Vitória A. R., Woerle de Lima T., Galvão-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.	Abstract
Sohraby F., Nunes-Alves A., Advances in computational methods for ligand binding kinetics. Trends in Biochemical Sciences, 2023, 48, 437-449.	Abstract
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, 2628–2643.	Abstract
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.	Abstract

2024

Last Updated on 04-09-2024 by Piotr Minkiewicz