Successful deep learning critically depends on the representation of the learned objects. Recent state-of-the-art pharmaceutical deep learning models successfully exploit graph-based de novo learning of molecular representations. Nonetheless, the combined potential of human expert knowledge of molecular representations and convolution neural networks has not been adequately explored for enhanced learning of pharmaceutical properties. Here we show that broader exploration of human-knowledge-based molecular representations enables more enhanced deep learning of pharmaceutical properties. By broad learning of 1,456 molecular descriptors and 16,204 fingerprint features of 8,506,205 molecules, a new feature-generation method MolMap was developed for mapping these molecular descriptors and fingerprint features into robust two-dimensional feature maps. Convolution-neural-network-based MolMapNet models were constructed for out-of-the-box deep learning of pharmaceutical properties, which outperformed the graph-based and other established models on most of the 26 pharmaceutically relevant benchmark datasets and a novel dataset. The MolMapNet learned important features that are consistent with the literature-reported molecular features.

成功的深度学习关键取决于学习对象的表示。最近最先进的药物深度学习模型成功地利用了基于图的分子表示从头学习。尽管如此，人类对分子表示和卷积神经网络的专家知识的综合潜力尚未得到充分探索，以增强对药物特性的学习。在这里，我们展示了对基于人类知识的分子表示的更广泛探索能够更增强对药物特性的深度学习。通过广泛学习 8,506,205 个分子的 1,456 个分子描述符和 16,204 个指纹特征，开发了一种新的特征生成方法 MolMap，用于将这些分子描述符和指纹特征映射到稳健的二维特征图中。基于卷积神经网络的 MolMapNet 模型是为开箱即用的药物特性深度学习而构建的，在 26 个与药物相关的基准数据集和一个新数据集的大多数上，该模型的性能优于基于图形的模型和其他已建立的模型。MolMapNet 学习了与文献报道的分子特征一致的重要特征。