对于由ANI-2X机器学习潜力预测的X射线配体,结合形态组合中的结合形态的分布
Fengyang Han1, Dongxiao Hao2, Xibing He1
1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Journal of chemical information and modeling
|October 30, 2023
概括
本研究引入了一种机器学习方法 (ANI-2X/CG-BS) 来分析连接体构造. 它揭示了结合的连接体构造在能量上是有利的,有助于虚拟选和对接策略.
科学领域:
- 计算化学计算化学
- 分子建模分子建模
- 药物发现 药物发现 药物发现
背景情况:
- 了解小分子联体的结构能量分布对于药物发现至关重要.
- 需要准确和高效的方法来探索连接体构成组合.
- 机器学习潜力为大规模分子模拟提供了一个有希望的途径.
研究的目的:
- 系统地研究生物活性联结体构成在它们组合中的能量分布.
- 为了评估ANI-2X机器学习潜力的性能,结合一个带有回溯线路搜索 (CG-BS) 算法的结合梯度.
- 在蛋白质数据库 (PDB) 中分析绑定和不绑定连接体构造之间的能量关系.
主要方法:
- 采用了ANI-2X机器学习潜力和结合梯度与回溯线索 (CG-BS) 几何优化算法.
- 在两个分子数据集上验证了ANI-2X/CG-BS方法与ab initio计算 (ωB97X/6-31G(d) 和B3LYP-D3BJ/DZVP).
- 使用OpenEye的Omega2软件为超过27,000个PDB连接体生成了广泛的构造合集.
主要成果:
- ANI-2X/CG-BS方法在评估形态能量方面表现出可靠的性能.
- 对17197个PDB配体的分析表明,50%的结合形态的相对能量低于2.91 kcal/mol,与全球最小形态相比.
- 大约90%的结合形状被发现在全球最低能量的10kcal/mol之内.
结论:
- 这项研究验证了ANI-2X/CG-BS作为一种有效的工具,用于探索连接体构造景观.
- 绑定联结体构造通常在它们的集合中具有能量可访问性.
- 这些发现可以指导虚拟选库的设计,并改进分子对接算法,以加强生物活性物质的采样.
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