通过可逆工作的早期丰富性命中发现c(t) 估计在元动力学 (CTMD) 中
bioRxiv : the preprint server for biology
|February 12, 2026
概括
我们介绍了基于c(t) 的元动力学 (CTMD),这是一个基于物理的快速协议,用于在药物发现查中进行早期丰富. CTMD准确地排列了小分子结合剂,优于人工智能方法,节省了大量资源.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 分子动力学分子动力学
背景情况:
- 小分子结合剂的虚拟选受到来自近似评分函数和刚性受体模型的错误阳性阻碍.
- 精确的自由能量计算可以解决这些局限性,但在计算上昂贵.
- 基于AI的共同折叠方法提供更低的成本,但缺乏一致的早期丰富性,并可能遭受记忆.
研究的目的:
- 引入基于物理的高通量协议,用于在击中选中进行早期丰富.
- 解决当前虚拟选和基于人工智能的联合折叠方法的局限性.
- 为排名小分子结合剂提供快速,准确和强大的替代方案.
主要方法:
- 开发了基于c(t) 的元动力学 (CTMD),一种使用非平衡可逆工作估计器c(t) 的协议.
- 在没有收的自由能量计算的情况下,CTMD从短,独立的温和的元动力学轨迹计算结合稳定性.
- 评估了CTMD在早期丰富的各种目标和化学型的表现.
主要成果:
- CTMD提供了强大的早期丰富性,优于基于AI的联合折叠方法,如Boltz-2,该方法显示丰富性与训练集相似性成比例.
- CTMD是快速的,可以通过最小的参数调整进行转移,并且可以抵抗记忆驱动的文物.
- 人工智能联合折叠方法表明,即使在活跃站点修改的情况下,性能也是如此.
结论:
- CTMD是一种可以立即部署的,基于物理的替代方法,用于药物查中的早期丰富.
- 它的简单性和有效性将其置于快速对接/人工智能方法和昂贵的免费能源计算之间.
- 预计CTMD将在药物发现活动中节省大量的财务和人力资本.
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