Abstract Convolutional neural network (CNN), is one of the most representative architectures in deep learning and is widely adopted in many fields especially in image classification and object detection. In the last few years, CNN has been aroused more and more attentions in drug discovery domain. In this work, molecular 2-D image-based CNN method was used to establish prediction models of the ADMET properties, including CYP1A2 inhibitory potency, P-glycoprotein (P-gp) inhibitory activity, Blood-Brain Barrier (BBB) penetrating activity, and Ames mutagenicity. The results showed that the predictive power of the established CNN models is comparable to that of the available machine learning models based on manual structural description and feature selection. It can be inferred that CNN can extract efficiently the key image features related to the molecular ADMET properties and offer a useful tool for virtual screening and drug design researches.

中文翻译：

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基于分子图像的卷积神经网络用于预测 ADMET 特性

摘要 卷积神经网络（CNN）是深度学习中最具代表性的架构之一，被广泛应用于许多领域，尤其是图像分类和目标检测。近年来，CNN在药物发现领域受到越来越多的关注。在这项工作中，基于分子二维图像的 CNN 方法被用于建立 ADMET 特性的预测模型，包括 CYP1A2 抑制效力、P-糖蛋白 (P-gp) 抑制活性、血脑屏障 (BBB) 穿透活性、和 Ames 致突变性。结果表明，所建立的 CNN 模型的预测能力与基于手动结构描述和特征选择的可用机器学习模型的预测能力相当。