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图书馆规模和测试规模对虚拟选的影响
Fangyu Liu1, Olivier Mailhot1, Isabella S Glenn1
1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Nature chemical biology
|January 3, 2025
概括
大型虚拟连接体库显著提高了药物发现成功率和效力. 从这些庞大的图书馆中测试更多的化合物可以发现更多的候选药物,并提高对它们进行有效排名的能力.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 用于药物发现的虚拟连接体库已经大幅扩展.
- 这些大型图书馆对命中率和化合物功效的影响仍然在很大程度上未经测试.
- 假设只有一小部分的对接命中,这使得对命中率改善的解释变得复杂.
研究的目的:
- 评估大规模虚拟查在识别强效药物连接物的有效性.
- 为了比较小型和大型虚拟选活动之间的命中率和支架多样性.
- 评估对接分数和已识别的连接体的实验验证之间的关系.
主要方法:
- 一个17亿个分子的虚拟库对抗beta-lactamase的对接.
- 对1521个新发现的分子进行实验测试.
- 与以前9900万个分子的屏幕进行比较,测试44种化合物.
主要成果:
- 在更大的虚拟屏幕上,命中率提高了两倍.
- 发现了更多的化学支架的多样性.
- 鉴定出50倍多的抑制剂,这表明巨大的未开发的配体潜力.
- 击中率和绑定亲和力与对接分数正相关.
结论:
- 扩展虚拟连接体库和随后的测试可以明显改善药物发现结果.
- 大型虚拟库包含的可行连接体数量明显高于目前测试的数量.
- 预计图书馆规模和测试规模的进一步增加将提高连接物识别和排名准确性.
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