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

By: Melo, Marcelo C.R [author.].
Contributor(s): Maasch, Jacqueline R. M. A. Cornell University [author.] | Fuente Nunez, Cesar de la. University of Pennsylvania [author.] | American Chemical Society.
Material type: materialTypeLabelBookSeries: ACS in focus: Publisher: Washington, DC, USA : American Chemical Society, 2022Description: 1 online resource : illustrations (some color).Content type: text Media type: computer Carrier type: online resourceISBN: 9780841299238.Subject(s): Artificial intelligence -- Medical applications | Drug development -- Data processing | Machine learning | Medical informatics | Drug Evaluation -- methodsDDC classification: 615.19 Online resources: Click here to access online
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.
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Includes bibliographical references and index.

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.

"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.

American Chemical Society, Machine Learning for Drug Discovery eBooks - 2022 Front Files.

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