Chapter 1Accessible Machine Learning Approaches for Toxicology

Sean Ekins1, Alex M. Clark2, Alexander L. Perryman3, Joel S. Freundlich3,4, 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

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 ...

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