title: Understanding the Roles of the "Two QSARs".
abstract: Quantitative structure-activity relationship (QSAR) modeling has matured over the past 50 years and has been very useful in discovering and optimizing drug leads. Although its roots were in extra-thermodynamic relationships within small sets of chemically similar molecules focused on mechanistic interpretation, a second class of QSAR models has emerged that relies on machine learning methods to generate models from large, chemically diverse data sets for predictive purposes. There has been a tension between the two groups of QSAR practitioners that is unnecessary and possibly counterproductive. This paper explains the difference in philosophy and application of these two distinct, but equally important, classes of QSAR models and how they can work together synergistically to accelerate the discovery of new drugs or materials.
authors.affiliation: Professor Emeritus at Kyoto University , 38-1 Iwakura-Miyakecho, Kyoto, Japan 606-0022.
authors.forename: Toshio
authors.initials: T
authors.lastname: Fujita
authors.affiliation: CSIRO Manufacturing , Bag 10, Clayton South MDC 3169, Australia.|Monash Institute of Pharmaceutical Sciences , 392 Royal Parade, Parkville 3052, Australia.|Latrobe Institute for Molecular Science, Latrobe University , Bundoora 3086, Australia.|School of Chemical and Physical Sciences, Flinders University , Bedford Park 5042, Australia.
authors.forename: David A
authors.initials: DA
authors.lastname: Winkler
mesh_terms: D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D013816:Thermodynamics