研究基于深度学习的药物分子生成的现状和前景
Huanghao Shi1, Zhichao Wang1, Litao Zhou1
1Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Current computer-aided drug design
|February 7, 2024
概括
深度学习通过克服传统方法的局限性,加速新药设计. 这种方法有效地产生具有理想性质的新型分子,推动制药研究的创新.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 人工智能在药物发现中的作用
背景情况:
- 传统的新分子生成方法面临着诸如合成难度和属性优化等挑战.
- 深度学习 (DL) 为设计新药候选药物提供了更有效和更合理的方法.
研究的目的:
- 提供基于深度学习的de novo药物设计的全面概述.
- 确定这一领域的关键研究领域和未来发展方向.
主要方法:
- 深度学习模型的审查,包括生成对抗网络 (GAN),循环神经网络 (RNN),变异自编码器 (VAE),卷积神经网络 (CNN) 和扩散模型 (DM).
- 基础方面的分析:用于培训的分子数据库,用于绩效的分子表示和用于质量评估的评估指标.
主要成果:
- 深度学习模型在产生具有特定性质的分子方面表现出显著的效率.
- 该研究强调了基于DL的新药设计的四个关键方面:数据,表示,模型和评估.
结论:
- 基于深度学习的de novo分子生成是一个快速发展的领域,吸引了大量的学术和工业兴趣.
- 对DL模型和方法的持续研究和开发对于未来的药物发现工作至关重要.
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