QSAR without borders

By Eugene N. Muratov, Jürgen Bajorath, Robert P. Sheridan, Igor V. Tetko, D. Filimonov, V. Poroikov, Tudor I. Oprea, Igor I. Baskin, Alexandre Varnek, Adrian E. Roitberg and 9 more. Chemical Society Reviews, 2020

546 citations recorded by the source corpus.

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. Importantly, it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas beyond traditional QSAR fields. These fields include nanotechnology, materials science, biomaterials, clinical informatics, and others. As modern research methods generate rapidly increasing amounts of data, knowledge of robust data-driven modelling methods is becoming essential for scientists in many disciplines both within and outside of chemical research. We hope that this contribution will serve to address this challenge.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/669a051c6c3754c72979d708e45cf5757343cc6f

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Citations in the corpus

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia (2022, unknown): “and Quantitative Structure–Activity Relationship [50].”
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (2020, background): “ 1;M 2; ;M ng, where each M i is a single, distinct model. For any input x, the ensemble’s prediction is dened to be the arithmetic mean of the individual models’ predictions: M~(x) = X M2E M(x) n : (2) Prior work has established that ensembling can yield models with greater accuracy than any indivi”
Discovery of a structural class of antibiotics with explainable deep learning (2023, background): “3e and Supplementary Data 2), which we experimentally tested.”