QSAR into the New Millennium: Structural, Topological, Quantum, Shape, Electronic and Bio Descriptors for Machine Learning & AI - Honoring Prof. Satya Prakash Gupta
Author:
Affiliation:
1. Department of Pharmaceutical Chemistry, KIET School of Pharmacy, KIET Group of Institutions, Delhi NCR, Ghaziabad, India
2. School of Molecular Sciences, Arizona State University, Tempe, AZ, 85287-1604, United States
Abstract
Publisher
Bentham Science Publishers Ltd.
Subject
Drug Discovery,General Medicine
Reference11 articles.
1. Hansch C.; Maloney P.P.; Fujita T.; Muir R.M.; Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients. Nature 1962,194,178-180
2. Balasubramanian K.; Combinatorics, Big Data, Neural Network and AI for Medicinal Chemistry and Drug Administration. Lett Drug Design Discov 2021,18(10),943-948
3. Uesawa Y.; AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP. Yakugaku Zasshi 2020,140(4),499-505
4. Mao J.; Akhtar J.; Zhang X.; Sun L.; Guan S.; Li X.; Chen G.; Liu J.; Jeon H.N.; Kim M.S.; No K.T.; Wang G.; Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience 2021,24(9),103052
5. Walters W.P.; Barzilay R.; Critical assessment of AI in drug discovery. Expert Opin Drug Discov 2021,16(9),937-947
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