Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
Andreas H. Göller, Lara Kuhnke, Floriane Montanari, Anne Bonin, Sebastian Schneckener, Antonius ter Laak, Joerg Wichard, Mario Lobell, Alexander Hillisch · Drug Discovery Today · 2020
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.