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.

在过去的二十年里，一个in silico拜耳制药创建了吸收、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。公司内的所有科学家都可以使用这些工具，并且可以帮助选择和设计新的潜在客户，以及优化潜在客户的过程。这里。我们讨论机器学习 (ML) 方法的发展，特别强调数据、描述符和算法。我们表明，高公司内部数据质量和量身定制的描述符，以及对实验终点的透彻理解，对于我们模型的效用至关重要。我们讨论了深度神经网络的近期影响并展示了选定的应用示例。