Transformer neural network for protein-specific de novo drug generation as a machine translation problem.
Transformer neural network for protein-specific de novo drug generation as a machine translation problem.
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DOI：
10.1038/s41598-020-79682-4
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发表时间：
2021-01-11
影响因子：
4.6
通讯作者：
Grechishnikova D
中科院分区：
文献类型：
--
作者：
Grechishnikova D
针对蛋白质靶点的药物发现是一个非常费力、漫长和昂贵的过程。机器学习方法，特别是深度生成网络，可以大大减少开发时间和成本。然而，大多数方法意味着蛋白质结合剂的先验知识，其物理化学特征或蛋白质的三维结构。在这项工作中提出的方法产生了新的分子，预测的能力，结合靶蛋白，仅依靠其氨基酸序列。我们认为靶向特异性从头药物设计是氨基酸“语言”和分子的简化输入线输入系统表示之间的翻译问题。为了解决这个问题，我们应用Transformer神经网络架构，一个国家的最先进的方法在序列转导任务。Transformer基于自注意技术，该技术允许捕获序列中项之间的长距离依赖关系。该模型生成具有结构新奇的现实多样的化合物。计算的物理化学性质和药物发现中使用的常用指标属于合理的药物样值范围。
Drug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative networks can substantially reduce development time and costs. However, the majority of methods imply prior knowledge of protein binders, their physicochemical characteristics or the three-dimensional structure of the protein. The method proposed in this work generates novel molecules with predicted ability to bind a target protein by relying on its amino acid sequence only. We consider target-specific de novo drug design as a translational problem between the amino acid “language” and simplified molecular input line entry system representation of the molecule. To tackle this problem, we apply Transformer neural network architecture, a state-of-the-art approach in sequence transduction tasks. Transformer is based on a self-attention technique, which allows the capture of long-range dependencies between items in sequence. The model generates realistic diverse compounds with structural novelty. The computed physicochemical properties and common metrics used in drug discovery fall within the plausible drug-like range of values.
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影响因子：
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