Computational Toxicology

Chapter 1Accessible Machine Learning Approaches for Toxicology

Sean Ekins¹, Alex M. Clark², Alexander L. Perryman³, Joel S. Freundlich^3,4, Alexandru Korotcov⁵ and Valery Tkachenko⁶

¹Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA

²Molecular Materials Informatics, Inc., Montreal, Quebec, Canada

³Department of Pharmacology & Physiology, New Jersey Medical School, Rutgers University, Newark, NJ, USA

⁴Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, New Jersey Medical School, Rutgers University, Newark, NJ, USA

⁵Gaithersburg, MD, USA

⁶Rockville, MD, USA

Chapter Menu

1. Introduction
2. Bayesian Models
3. Deep Learning Models
4. Comparison of Different Machine Learning Methods
5. Future Work

1.1 Introduction

Computational approaches have in recent years played an increasingly important role in the drug discovery process within large pharmaceutical firms. Virtual screening of compounds using ligand-based and structure-based methods to predict potency enables more efficient utilization of high throughput screening (HTS) resources, by enriching the set of compounds physically screened with those more likely to yield hits [1–4]. Computation of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties exploiting statistical techniques greatly reduces the number of expensive assays that must be performed, now making it practical to consider these factors very early in the discovery ...