对于 de novo 药物设计的生成深度学习――化学空间奥德赛
Rıza Özçelik1, Helena Brinkmann1, Emanuele Criscuolo1
1Department of Biomedical Engineering, Institute for Complex Molecular System (ICMS) and Eindhoven AI Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven 5612 AZ, Netherlands.
Journal of chemical information and modeling
|July 9, 2025
概括
生成型深度学习通过创建新分子为药物设计提供了新的途径. 这种方法在平衡化学多样性,合成性和生物活性方面面临挑战,以有效的药物发现.
科学领域:
- 医药化学中的人工智能
- 计算机化药物发现.
- 使用深度学习的分子生成.
背景情况:
- 生成型深度学习模型正在彻底改变药物设计.
- 探索广的化学空间以识别新药候选药物至关重要.
- 平衡多种分子性质是一个重大的挑战.
研究的目的:
- 检查在药物发现中应用生成模型的机会和挑战.
- 专注于小分子生成,评估和优先级.
- 概述药物设计中生成性深度学习的未来方向.
主要方法:
- 对目前用于小分子生成的生成方法的审查.
- 讨论生成模型的优化策略.
- 强调需要强有力的评估协议.
主要成果:
- 生成型模型在探索化学空间以寻找新药性质方面表现有前途.
- 导航关于化学多样性,合成性和生物活性的相互矛盾数据是关键.
- 目前的方法需要改进和严格的评估.
结论:
- 生成型深度学习是药物设计化学旅程的强大工具.
- 解决固有的困境对于成功应用至关重要.
- 需要进一步的研究才能充分利用其在创造新疗法的潜力.
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