Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
| Autor: | Lara Kuhnke, Floriane Montanari, Mario Lobell, Sebastian Schneckener, Jörg Wichard, Alexander Hillisch, Andreas H. Göller, Antonius Ter Laak, Anne Bonin |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Process (engineering) Computer science In silico Machine learning computer.software_genre Intestinal absorption Machine Learning 03 medical and health sciences 0302 clinical medicine Drug Discovery Animals Humans Computer Simulation Pharmacokinetics Pharmacology business.industry Models Theoretical Variety (cybernetics) 030104 developmental biology Intestinal Absorption Pharmaceutical Preparations 030220 oncology & carcinogenesis Data quality Deep neural networks Artificial intelligence business computer |
| Zdroj: | Drug Discovery Today. 25:1702-1709 |
| ISSN: | 1359-6446 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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