Accessible Machine Learning Approaches for Toxicology
Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru V. Korotcov, Valery Tkachenko · Computational Toxicology · 2018
This chapter explores machine learning algorithms and makes them accessible for seeding drug discovery projects. It explores the authors's novel data pruning strategy when constructing Bayesian models to predict other types of properties. The chapter summarizes the application of the machine learning methods to toxicology datasets and transporters. Support vector machine (SVM) is one of the most popular supervised machine learning algorithms used mostly in classification problems and it is quite effective in high-dimensional spaces. The deep learning (DL) model is trained with a dataset by adjusting the weights to give the response expected for a certain input. The chapter compares deep neural networks (DNNs) and classic machine learning (CML) methods with different datasets of toxicological relevance for future embedding into the pen science data repository (OSDR). Collaborative Drug Discovery (CDD) model hosts the software and customers' data vaults on its secure servers.