QSAR without borders
University of North Carolina at Chapel Hill · Universidade Federal da Paraíba · +34 more institutions
Abstract
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…
Citation impact
- FWCI
- 36.57
- Percentile
- 100%
- References
- 345
Authors
19- EMEugene Muratov
University of North Carolina at Chapel Hill, Universidade Federal da Paraíba, Communities In Schools of Orange County
- JBJürgen Bajorath
University of Bonn
- RPRobert P. Sheridan
Merck & Co., Inc., Rahway, NJ, USA (United States)
- IVIgor V. Tetko
Helmholtz Zentrum München, Institute of Structural and Molecular Biology
- DFDmitry Filimonov
Institute of Biomedical Chemistry
Topics & keywords
- Quantitative structure–activity relationship
- Computer science
- Machine learning