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首页> 外文期刊>Journal of Computer-Aided Molecular Design >Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images
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Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images

机译：用于从分子图像预测体外清除终点的多任务卷积神经网络

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摘要

Abstract Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed up the progression of novel candidate molecules. Herein, we have investigated the question if multiple in vitro clearance endpoints could be accurately predicted from image-based molecular representations. Thus, compound measurements for four commonly investigated clearance endpoints were curated from AstraZeneca internal sources, providing a sound basis for building multi-task convolutional neural network models. Application of several increasingly challenging data splitting strategies confirmed that convolutional neural network models were successful at capturing implicit chemical relationships contained in training and test data, similar to what is commonly observed for structural fingerprints. Furthermore, model benchmarking against state-of-the-art machine learning methods, including deep neural networks and graph convolutional neural networks, trained with structure- and graph-based representations, respectively, revealed on par or increased accuracy of convolutional neural networks with clear benefit of multi-task learning across all clearance endpoints. Our findings indicate that image-based molecular representations can be applied to predict multiple clearance endpoints, suggesting a potential follow-up to investigate model interpretability from molecular images.
机译：摘要 化合物代谢稳定性的优化是药物研究中一个备受关注的问题。因此，在计算机模型中应用预测可以潜在地减少设计-制造-测试-分析迭代的次数，从而加快新型候选分子的进展。在此，我们研究了是否可以从基于图像的分子表示中准确预测多个体外清除终点的问题。因此，从阿斯利康内部来源中整理了四个常用研究的清除终点的化合物测量值，为构建多任务卷积神经网络模型提供了坚实的基础。几种越来越具有挑战性的数据拆分策略的应用证实，卷积神经网络模型成功地捕获了训练和测试数据中包含的隐式化学关系，类似于通常观察到的结构指纹。此外，与最先进的机器学习方法（包括深度神经网络和图卷积神经网络）进行模型基准测试，分别使用基于结构和图形的表示进行训练，揭示了卷积神经网络的精度相当或更高，并且在所有清除端点上具有多任务学习的明显优势。我们的研究结果表明，基于图像的分子表示可用于预测多个清除终点，这表明可以进行后续研究，以研究分子图像的模型可解释性。

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