新兴的基于结构的计算方法来选爆炸式可访问的化学空间
Corentin Bedart1, Conrad Veranso Simoben2, Matthieu Schapira3
1Univ. Lille, Inserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, F-59000, Lille, France.
Current opinion in structural biology
|April 11, 2024
概括
基于结构的虚拟查通过预测蛋白质相互作用来识别候选药物. 新的机器学习和基于synthon的方法加速了大量化学图书馆的选,克服了药物发现的计算限制.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 化学信息学 化学信息学
背景情况:
- 基于结构的虚拟查 (SBVS) 对于通过预测它们与标蛋白的相互作用来识别潜在的候选药物至关重要.
- 化学图书馆的指数增长带来了计算挑战,因为选能力落后于不断扩大的可访问分子数量.
研究的目的:
- 对加速虚拟选的新,越来越受欢迎的方法进行审查.
- 讨论机器学习加速和基于synthon的图书馆选方法.
主要方法:
- 对开创性的概念验证研究进行审查.
- 关于加速虚拟查技术的最新发展情况的摘要.
- 讨论局限性和未来的研究方向.
主要成果:
- 机器学习和基于synthon的方法提供解决方案,以更高效地计算选大型化学库.
- 这些方法提高了在庞大的数据集中识别高质量的化合物的能力.
结论:
- 加快的虚拟选方法对于现代药物发现至关重要,能够有效地探索广的化学空间.
- 未来的方向包括进一步开发和整合这些计算策略,以克服选瓶.
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