基于结构的虚拟查的科学和艺术
1Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.
ACS medicinal chemistry letters
|April 17, 2024
概括
基于结构的虚拟查 (SBVS) 对药物发现至关重要,但其成功取决于图书馆质量和计算方法. 来自八个活动的经验教训指导选择验证的虚拟命中,提高命中发现率.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 药品化学 药品化学 是一个
背景情况:
- 药物发现中的高消耗率需要探索更大的化学空间.
- 基于结构的虚拟选 (SBVS) 正在作为一个关键战略重新获得突出地位.
- 贝叶斯观点强调了可持续的命中发现的局限性.
研究的目的:
- 讨论SBVS作为可持续的命中发现策略的缺点.
- 分析先前的命中率和计算方法性能对SBVS的影响.
- 分享从SBVS成功的实验验证活动中学到的实践经验.
主要方法:
- 对虚拟查的贝叶斯观点的审查.
- 在选图书馆中分析先前的命中率.
- 在SBVS中计算方法性能的评估.
- 来自8个成功的SBVS活动的案例研究.
主要成果:
- 从贝叶斯观点确定SBVS的缺点.
- 之前的命中率和计算性能极大地影响了SBVS的成功.
- 八个活动为虚拟热门选择提供了有价值的见解.
- 一个活动为现在正在临床试验中的候选药物做出了贡献.
结论:
- SBVS需要仔细考虑图书馆质量和计算工具,以实现可持续的命中发现.
- 从实践活动中吸取的经验教训增强了用于验证的虚拟成功的选择.
- 优化SBVS策略可以显著影响候选药物进展.
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