Machine Learning for Drug Discovery

Marcelo C. R. Melo · Jacqueline R. M. A. Maasch · Cesar de la Fuente-Nunez

2022 թ. մրտ · ACS In Focus Գիրք 6 · American Chemical Society

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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.

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Marcelo C. R. Melo is a computational biologist focused on integrating multiple-scale experimental observations into computational models that improve our understanding of biological systems. His work ranges from atomistic and quantum-chemical simulations to whole-cell models of gene expression and metabolism, integrating machine learning to extract information from large-scale datasets. He obtained a bachelor’s and a master’s degree in Biophysics from the Federal University of Rio de Janeiro, Brazil, before joining the University of Illinois at Urbana-Champaign to pursue a Ph.D. in Biophysics and Computational Biology. During that time, he was awarded the CompGen Fellowship for research at the interface of Biology and High-Performance Computing, funded by the Institute for Genomic Biology and the National Center for Supercomputing Applications. He later joined the University of Pennsylvania as a Postdoctoral Researcher to lead the computational core of the Machine Biology Group. Marcelo is currently working on drug development to help reduce the impact of infectious and neurological conditions.

Jacqueline R. M. A. Maasch is a PhD student in the Department of Computer Science at Cornell University. They received their master’s degree in computer science from the University of Pennsylvania and their bachelor’s degree from Smith College. Their research investigates novel machine learning methods for biomedical discovery, with an emphasis in computational drug discovery. They are the recipient of the National Science Foundation Graduate Research Fellowship and the Cornell Presidential Life Science Fellowship.

Cesar de la Fuente-Nunez is a Presidential Assistant Professor at the University of Pennsylvania. Prof. de la Fuente has received more than 50 awards. He was recognized by MIT Technology Review as one of the world’s top innovators, selected as the inaugural recipient of the Langer Prize, and received the ACS Infectious Diseases Young Investigator Award. In 2021, he received the prestigious Princess of Girona Prize for Scientific Research, the Thermo Fisher Award, and the EMBS Academic Early Career Achievement Award \for the pioneering development of novel antibiotics designed using principles from computation, engineering, and biology." Prof. de la Fuente has given more than 150 invited lectures, is an inventor on multiple patents, and has published around 100 publications, including papers in Nature Biomedical Engineering, Nature Communications, PNAS, and ACS Nano.