论文标题
通过深度学习和古典编程的摩尔摩拉菌的研究和开发
Research and development of MolAICal for drug design via deep learning and classical programming
论文作者
论文摘要
深度学习方法已渗透到计算机辅助药物设计的研究领域。深度学习生成模型和经典算法可以同时用于受体3D袋中的三维（3D）药物设计。在这里，用于药物设计的三个方面：在第一部分中，摩尔语使用了遗传算法，Vinardo评分和深度学习生成模型，该模型受到生成对抗性网（GAN）的训练，用于药物设计。在第二部分中，深度学习生成模型是通过药物数据库（例如锌数据库）的药物样分子来训练的。该摩尔语将自动筛选用于药物虚拟筛查的深度学习生成模型和分子对接。在第三部分中，添加了有用的药物工具来计算相对特性，例如泛 - 测定干扰化合物（痛苦），lipinski的五个规则，合成可及性（SA）等。此外，还嵌入了结构相似性搜索和定量结构 - 活性关系（QSAR）等，以计算摩拉利亚药物特性的计算。 Molaical将不断优化并开发用于药物设计的当前和新模块。摩尔语可以帮助科学家，药剂师和生物学家通过深度学习模型和经典编程在受体口袋中设计合理的3D药物。 Molaical可用于任何学术和教育目的，可以从网站https://molaical.github.io下载。
Deep learning methods have permeated into the research area of computer-aided drug design. The deep learning generative model and classical algorithm can be simultaneously used for three-dimensional (3D) drug design in the 3D pocket of the receptor. Here, three aspects of MolAICal are illustrated for drug design: in the first part, the MolAICal uses the genetic algorithm, Vinardo score and deep learning generative model trained by generative adversarial net (GAN) for drug design. In the second part, the deep learning generative model is trained by drug-like molecules from the drug database such as ZINC database. The MolAICal invokes the deep learning generative model and molecular docking for drug virtual screening automatically. In the third part, the useful drug tools are added for calculating the relative properties such as Pan-assay interference compounds (PAINS), Lipinski's rule of five, synthetic accessibility (SA), and so on. Besides, the structural similarity search and quantitative structure-activity relationship (QSAR), etc are also embedded for the calculations of drug properties in the MolAICal. MolAICal will constantly optimize and develop the current and new modules for drug design. The MolAICal can help the scientists, pharmacists and biologists to design the rational 3D drugs in the receptor pocket through the deep learning model and classical programming. MolAICal is free of charge for any academic and educational purposes, and it can be downloaded from the website https://molaical.github.io.
