基于结构的自动化新药设计的最新进展
Yidan Tang1, Rocco Moretti1,2, Jens Meiler1,2,3
1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Journal of chemical information and modeling
|March 14, 2024
概括
基于结构的de novo药物设计加快了使用计算方法寻找新药的速度. 本综述涵盖了最近的进展,包括深度学习,并强调改善药物发现的合成可访问性.
科学领域:
- 计算化学和化学信息学
- 药物发现和药物化学
背景情况:
- 越来越多的蛋白质结构和化学库需要更快的药物设计方法.
- 基于传统结构的计算机辅助药物设计往往耗时.
- 在新药设计中,使用in silico启发学来加快化学空间的探索.
研究的目的:
- 审查基于结构的新药设计算法的最新进展.
- 要突出努力改善合成生成分子的可访问性.
- 讨论用于验证新药设计框架的基准测试策略.
主要方法:
- 关于基于片段的方法,进化算法,大都市蒙特卡洛和深度生成模型的文献评论.
- 在每个方法中分析合成可访问性考虑因素.
- 检查验证和基准测试方法.
主要成果:
- 最近的进展包括与传统方法一起应用深度生成模型.
- 显著的重点是加强设计的候选药物的实际合成.
- 为了评估 de novo 设计框架的性能,采用了各种基准分析策略.
结论:
- 基于结构的de novo药物设计正在随着新的计算方法而发展.
- 提高合成可访问性仍然是一个关键的挑战和重点领域.
- 强大的验证对于这些药物设计工具的成功实施至关重要.
关键词:
人工智能的人工智能是人工智能.计算机辅助药物设计在新的药物设计中.进化算法是一种进化算法.碎片正在成长中的碎片.基于碎片的连接体设计.遗传算法 遗传算法 遗传算法机器学习 机器学习基于结构的药物设计.合成可访问性 合成可访问性更多相关视频
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