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Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2022-05-07 , DOI: 10.1186/s13321-022-00603-w
Doha Naga 1, 2 , Wolfgang Muster 1 , Eunice Musvasva 1 , Gerhard F Ecker 2
Affiliation  

Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

中文翻译:

Off-targetP ML:用于小分子脱靶面板安全评估的开源机器学习框架

根据几项研究,不可预测的药物安全问题构成了制药行业的大部分失败。这些临床前安全性问题中的一些可归因于化合物与其预期治疗靶标以外的靶标的非选择性结合,从而导致不希望的不良事件。因此,制药公司通常会在临床前和临床研究之前进行体外安全筛查,以检测脱靶活动。因此,我们提出了一个开源机器学习框架,旨在直接从它们的结构预测我们内部 50 种针对约 4000 种化合物的脱靶小组活动。该框架旨在指导化学家在合成前进行药物设计过程并加速药物发现。我们还提出了一组 ML 方法,这些方法需要最少的编程经验才能进行部署。该工作流程结合了不同的机器学习方法,例如深度学习和自动化机器学习。它还解决了生物活性预测中面临的常见问题,如数据不平衡、目标间重复测量和重复的公共化合物标识符。在整个工作流程开发过程中,我们探索和比较了神经网络和 AutoML 在为不同蛋白质类别、不同数据集大小和高级不平衡的 50 个脱靶构建预测模型方面的能力。在效率和功效方面比较了不同方法的结果。
更新日期:2022-05-09
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