Cui Qiuji, Lu Shuai, Ni Bingwei, Zeng Xian, Tan Ying, Chen Ya Dong, Zhao Hongping
School of Science, China Pharmaceutical University, Nanjing, China.
Department of Biological Medicine, School of Pharmacy, Fudan University, Shanghai, China.
Front Oncol. 2020 Feb 11;10:121. doi: 10.3389/fonc.2020.00121. eCollection 2020.
Aqueous solubility is an important physicochemical property of compounds in anti-cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning methods. The reported performances vary significantly partly because of the different datasets used. Solubility prediction on novel compounds needs to be improved, which may be achieved by going deeper with deep learning. We constructed deeper-net models of ~20-layer modified ResNet convolutional neural network architecture, which were trained and tested with 9,943 compounds encoded by molecular fingerprints. Retrospectively tested by 62 recently-published novel compounds, one deeper-net model outperformed four established tools, shallow-net models, and four human experts. Deeper-net models also outperformed others in predicting the solubility values of a series of novel compounds newly-synthesized for anti-cancer drug discovery. Solubility prediction may be improved by going deeper with deep learning. Our deeper-net models are accessible at http://www.npbdb.net/solubility/index.jsp.
水溶性是抗癌药物研发中化合物的一项重要物理化学性质。人工智能溶解度预测工具通过采用回归、机器学习和深度学习方法取得了令人瞩目的成绩。所报告的性能差异很大，部分原因是使用了不同的数据集。对新型化合物的溶解度预测有待改进，这可能通过深化深度学习来实现。我们构建了约20层的改进型ResNet卷积神经网络架构的深度网络模型，并用分子指纹编码的9943种化合物对其进行训练和测试。通过62种最近发表的新型化合物进行回顾性测试，一个深度网络模型优于四种既定工具、浅层网络模型和四位人类专家。深度网络模型在预测为抗癌药物研发新合成的一系列新型化合物的溶解度值方面也优于其他模型。通过深化深度学习可以改进溶解度预测。我们的深度网络模型可在http://www.npbdb.net/solubility/index.jsp获取。
