Melo, Marcelo C.R.,
Machine learning for drug discovery / Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez. - 1 online resource : illustrations (some color). - ACS in focus, 2691-8307 . - ACS in focus, .
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."--
9780841299238
10.1021/acsinfocus.7e5017 doi
Artificial intelligence--Medical applications.
Drug development--Data processing.
Machine learning.
Medical informatics.
Drug Evaluation--methods.
R859.7.A78 / M446 2022eb
615.19
W26.55.A7 / M446 2022eb
Machine learning for drug discovery / Marcelo C.R. Melo, Jacqueline R. M. A. Maasch & Cesar de la Fuente Nunez. - 1 online resource : illustrations (some color). - ACS in focus, 2691-8307 . - ACS in focus, .
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."--
9780841299238
10.1021/acsinfocus.7e5017 doi
Artificial intelligence--Medical applications.
Drug development--Data processing.
Machine learning.
Medical informatics.
Drug Evaluation--methods.
R859.7.A78 / M446 2022eb
615.19
W26.55.A7 / M446 2022eb