Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096 Wuppertal, Germany.
Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 13342 Berlin, Germany.
Drug Discov Today. 2020 Sep;25(9):1702-1709. doi: 10.1016/j.drudis.2020.07.001. Epub 2020 Jul 9.
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.
在过去的二十年中，拜耳制药公司创建了一个计算机辅助的吸收、分布、代谢和排泄（ADMET）平台，旨在为早期药物发现中的各种药代动力学和物理化学终点生成模型。这些工具对公司内的所有科学家都开放，可以帮助选择和设计新型先导化合物，并优化先导化合物。在这里，我们讨论了机器学习（ML）方法的发展，特别强调了数据、描述符和算法。我们表明，公司内部高的数据质量和定制的描述符，以及对实验终点的透彻理解，对于我们模型的实用性至关重要。我们讨论了深度神经网络的最新影响，并展示了一些应用实例。
