The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .

中文翻译：

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FP-ADMET：基于指纹的 ADMET 预测模型纲要

药物的吸收、分布、代谢、排泄和毒性 (ADMET) 在确定优先考虑哪些潜在候选药物方面起着关键作用。基于机器学习方法的计算机方法正变得越来越流行，但仍然受到数据可用性的限制。为了向科学界提供数据和模型，我们开发了 FPADMET，它是基于分子指纹的 ADMET 属性预测模型库。在本文中，我们研究了基于指纹的机器学习模型对大量 ADMET 相关属性的有效性。一组 20 个不同二进制指纹的预测能力（基于子结构键、原子对、本地路径环境、以及自定义指纹（例如全最短路径）的 50 多个 ADMET 和 ADMET 相关终点已作为研究的一部分进行了评估。我们发现，对于大多数属性，与传统的 2D/3D 分子描述符相比，基于指纹的随机森林模型产生可比或更好的性能。这些模型作为开放访问软件的一部分提供，可以从 https://gitlab.com/vishsoft/fpadmet 下载。