Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field
Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field
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DOI：
10.1021/ja512751q
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发表时间：
2015-02-25
影响因子：
15
通讯作者：
Abel, Robert
中科院分区：
文献类型：
--
作者：
Wang, Lingle;Wu, Yujie;Abel, Robert
设计。紧密结合的配体是小分子药物开发的主要目标。在过去的几十年里，自由能计算受益于力场和采样算法的改进，以及低成本并行计算的出现。然而，事实证明，要可靠地达到指导广泛配体和蛋白质靶标的先导优化所需的准确性水平(类似于结合亲和力的5倍)是具有挑战性的。不足为奇的是，由于缺乏大规模验证，再加上传统上与运行这些类型的计算相关的技术挑战，自由能模拟的广泛商业应用一直受到限制。在这里，我们报告了一种方法，该方法在广泛的目标类别和配体上实现了前所未有的准确度，追溯结果包括200个配体和各种化学扰动，其中许多涉及配体化学结构的重大变化。此外，我们在未来的药物发现项目中应用了这种方法，发现合成的化合物的质量有了显著的提高，这些化合物被预测为有效的。与其他计算或药物化学方法合成的化合物相比，通过这种方法预测有效的化合物有很大的假阳性减少。此外，结果与我们的回溯性研究结果一致，证明了该方法的稳健性和广泛的适用性，可用于推动销售线索优化决策。
Designing. tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (similar to 5x in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the Method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent, by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from Our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.