Title Machine learning for drug discovery / Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez

Published Washington, DC, USA : American Chemical Society, 2022

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Description 1 online resource : illustrations (some color) Series ACS in focus, 2691-8307 ACS in focus, 2691-8307 Contents Pursuing New Models and Molecules -- Key Algorithms for Drug Discovery -- Data Representation in Computational Chemistry -- Drug-likeness Prediction -- Antimicrobial Activity Prediction -- Antimicrobial Resistance Prediction -- Generative Deep Learning for Drug Discovery -- Future Directions Summary "Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included."-- Provided by publisher Bibliography Includes bibliographical references and index Subject Artificial intelligence -- Medical applications. Drug development -- Data processing Machine learning. Medical informatics. Drug Evaluation -- methods Machine Learning Artificial intelligence -- Medical applications Machine learning Medical informatics Form Electronic book Author Maasch, Jacqueline R. M. A., author. Cornell University Fuente Nunez, Cesar de la, author. University of Pennsylvania American Chemical Society. ISBN 9780841299238 0841299234

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