QSAR without borders

Eugene N Muratov, Jürgen Bajorath, Robert P Sheridan, Igor V Tetko, Dmitry Filimonov, Vladimir V Poroǐkov, Tudor I Oprea, Igor I Baskin, Alexandre Varnek, Adrián Enrique Roitberg, Olexandr Isayev, Stefano Curtalolo, Denis Fourches, Yoram Cohen, Alan Aspuru-Guzik, David A. Winkler, Dimitris K Agrafiotis, Artem R Cherkasov, Alexander Tropsha

Research output: Contribution to journalArticleResearchpeer-review

352 Citations (Scopus)


Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Original languageEnglish
Pages (from-to)3525-3564
Number of pages40
JournalChemical Society Reviews
Issue number11
Publication statusPublished - 1 May 2020

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