Marta Lettieri, Virginia Carlucci and Marco Rodda have contributed to the latest edition of Computational Toxicology – Methods and Protocols, to be published by Springer this month!
On October 25th, Springer released the second edition of the book entitled Computational Toxicology – Methods and Protocols, as part of the book series Methods in Molecular Biology, edited by Professor Orazio Nicolotti.
First Edition:
The first edition of Computational Toxicology – Methods and Protocols was published in 2018 and edited by Professor Orazio Nicolotti. This edition explores techniques that have been used to understand solid target-specific models in computational toxicology. The chapters are divided into four sections and discuss molecular descriptors, QSAR and read-across; molecular and data modeling techniques to comply with both scientific and regulatory sides; computational toxicology in drug discovery; and strategies on how to predict various human-health toxicology endpoints.
Second Edition:
The second edition of Computational Toxicology explores new and updated techniques used to understand solid target-specific models in computational toxicology. Chapters are grouped into four sections, detailing updates in molecular descriptors, QSAR and read-across, molecular and data modeling techniques, computational toxicology in drug discovery, molecular fingerprints, AI techniques, and safe drug design. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.
Chemsafe’s Contribution to the Book:
Chemsafe has edited one of the eighteen chapters of this book: the thirteenth chapter entitled Machine learning in early prediction of metabolism of drugs.
This chapter is about how Machine Learning (ML) has increasingly been applied to predict properties of drugs, particularly, drug metabolism and biotransformation reactions. Metabolism prediction can be addressed with the application of ML models trained on large and validated data set, from early stages of lead optimization to latest stage of drug development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3Rs principles within the Big Data era.
