Journal
CHEMICAL SOCIETY REVIEWS
Volume 49, Issue 11, Pages 3525-3564Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0cs00098a
Keywords
-
Categories
Funding
- NIH [U01CA207160, U24CA224370, U24TR002278, U01CA239108]
- Russian Program for Basic Research of State Academies of Sciences
- European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant [676434]
- NSF [CHE-1802831, CHE-1802789]
- Army Research Office (ARO) [W911NF1810315]
- U.S. Department of Defense (DOD) [W911NF1810315] Funding Source: U.S. Department of Defense (DOD)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available