TY - JOUR
T1 - QSAR without borders
AU - Muratov, Eugene N
AU - Bajorath, Jürgen
AU - Sheridan, Robert P
AU - Tetko, Igor V
AU - Filimonov, Dmitry
AU - Poroǐkov, Vladimir V
AU - Oprea, Tudor I
AU - Baskin, Igor I
AU - Varnek, Alexandre
AU - Roitberg, Adrián Enrique
AU - Isayev, Olexandr
AU - Curtalolo, Stefano
AU - Fourches, Denis
AU - Cohen, Yoram
AU - Aspuru-Guzik, Alan
AU - Winkler, David A.
AU - Agrafiotis, Dimitris K
AU - Cherkasov, Artem R
AU - Tropsha, Alexander
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086284414&partnerID=8YFLogxK
U2 - 10.1039/D0CS00098A
DO - 10.1039/D0CS00098A
M3 - Article
C2 - 32356548
AN - SCOPUS:85086284414
SN - 0306-0012
VL - 49
SP - 3525
EP - 3564
JO - Chemical Society Reviews
JF - Chemical Society Reviews
IS - 11
ER -