通过深度学习和经典编程研究和开发用于药物设计的MolAICal
Research and development of MolAICal for drug design via deep learning and classical programming
摘要：
深度学习方法已经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口袋中的3D药物设计。在这里，阐述了MolAICal用于药物设计的三个方面：在第一部分中，MolAICal使用遗传算法，Vinardo评分和由生成对抗网（GAN）训练的深度学习生成模型进行药物设计。在第二部分中，深度学习生成模型由来自药物数据库（如ZINC数据库）的药物样分子训练。MolAICal自动调用深度学习生成模型和分子对接进行药物虚拟筛选。在第三部分中，我们增加了药物相关性质的计算工具，如PAINS、Lipinski's rule of five、合成可及性（SA）等，并将结构相似性搜索和定量构效关系（QSAR）等嵌入到MolAICal中。MolAICal将不断优化和开发用于药物设计的现有和新模块。MolAICal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设计受体口袋中的合理3D药物。MolAICal对于任何学术和教育目的都是免费的，可以从网站https://molaical.github.io下载。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
