Bayer's in silico ADMET platform: a journey of machine learning over the past two decades. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: Göller, Andreas H.; Kuhnke, Lara; Montanari, Floriane; Bonin, Anne; Schneckener, Sebastian; ter Laak, Antonius; Wichard, Jörg; Lobell, Mario; Hillisch, Alexander
• Is Part Of: Drug discovery today. Volume 25:Issue 9(2020)
• Page Start: 1702
• Page End: 1709
• Publication Date: 2020-09
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2020.07.001
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Evolution of Bayer's in silico ADMET platform, modelling pharmacokinetic and physicochemical endpoints. Application of machine learning, deep learning and artificial intelligence in drug discovery projects. Triade of machine learning consisting of data, descriptors and algorithms. Summary of 20 years of experience in developing machine learning in silico ADMET models. Importance of high data quality, tailored descriptors, regular model retraining and visualization. Abstract : Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.