Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit
Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit
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DOI：
10.1021/acs.jcim.2c01526
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发表时间：
2023-04
影响因子：
5.6
通讯作者：
Edison Mucllari;Vasily Zadorozhnyy;Qiang Ye;D. Nguyen
中科院分区：
文献类型：
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作者：
Edison Mucllari;Vasily Zadorozhnyy;Qiang Ye;D. Nguyen
深度神经网络（DNN）的进步为许多研究领域的研究人员提供了一种非常强大的机器学习方法，包括生物医学和化学信息学社区，其中DNN有助于改善蛋白质性能，分子设计，药物发现等任务。尽管做出了重大努力并引入了许多方法来获得分子描述符，但分子性质的定量预测仍然具有挑战性。将分子特征编码成比特串的一种广泛使用的方法是分子指纹。在这项工作中，我们提出在神经网络编码器（AutoEncoder）内部使用新的Neumann-Cayley门控递归单元（NC-GRU）来创建神经分子指纹（NC-GRU指纹）。NC-GRU AutoEncoder将正交权重引入到广泛使用的GRU架构中，从而实现更快，更稳定的训练和更可靠的分子指纹。集成新型NC-GRU指纹和多任务DNN原理图可以提高各种分子相关任务的性能，如毒性，分配系数，亲油性和溶剂化自由能，在多个基准测试中产生最先进的结果。
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein performance, molecular design, drug discovery, etc. Many of those tasks rely on molecular descriptors for representing molecular characteristics in cheminformatics. Despite significant efforts and the introduction of numerous methods that derive molecular descriptors, the quantitative prediction of molecular properties remains challenging. One widely used method of encoding molecule features into bit strings is the molecular fingerprint. In this work, we propose using new Neumann-Cayley Gated Recurrent Units (NC-GRU) inside the Neural Nets encoder (AutoEncoder) to create neural molecular fingerprints (NC-GRU fingerprints). The NC-GRU AutoEncoder introduces orthogonal weights into widely used GRU architecture, resulting in faster, more stable training, and more reliable molecular fingerprints. Integrating novel NC-GRU fingerprints and Multi-Task DNN schematics improves the performance of various molecular-related tasks such as toxicity, partition coefficient, lipophilicity, and solvation-free energy, producing state-of-the-art results on several benchmarks.