工业规模的多任务 ADME/PK 预测:利用大型多样的实验数据集。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Molecular Informatics Pub Date : 2024-10-01 Epub Date: 2024-07-08 DOI:10.1002/minf.202400079
Moritz Walter, Jens M Borghardt, Lina Humbeck, Miha Skalic
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引用次数: 0

摘要

ADME(吸收、分布、代谢、排泄)特性是判断候选药物是否具有理想药代动力学(PK)特征的关键参数。在这项研究中,我们测试了多任务机器学习(ML)模型,这些模型是根据勃林格殷格翰公司内部生成的数据训练而成的,用于预测 ADME 和动物 PK 终点。我们在化合物设计阶段(即没有测试化合物的实验数据)和测试阶段(即可能有早期进行的实验数据)对模型进行了评估。利用现实的时间分割,我们发现基于图的多任务神经网络模型的性能明显优于单任务模型。为了解释多任务模型的成功,我们发现数据点数量最多的终点(理化终点、微粒体中的清除率)尤其能提高更复杂的 ADME 和 PK 终点的预测能力。总之,我们的研究深入探讨了如何充分利用制药公司的多个 ADME/PK 终点数据来优化多重任务模型的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets.

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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