Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise Differences

Alchemical binding free energy (BFE)
calculations offer an efficient
and thermodynamically rigorous approach to in silico binding affinity
predictions. As a result of decades of methodological improvements
and recent advances in computer technology, alchemical BFE calculations
are now widely used in drug discovery research. They help guide the
prioritization of candidate drug molecules by predicting their binding
affinities for a biomolecular target of interest (and potentially
selectivity against undesirable antitargets). Statistical variance
associated with such calculations, however, may undermine the reliability
of their predictions, introducing uncertainty both in ranking candidate
molecules and in benchmarking their predictive accuracy. Here, we
present a computational method that substantially improves the statistical
precision in BFE calculations for a set of ligands binding to a common
receptor by dynamically allocating computational resources to different
BFE calculations according to an optimality objective established
in a previous work from our group and extended in this work. Our method,
termed Network Binding Free Energy (NetBFE), performs adaptive BFE
calculations in iterations, re-optimizing the allocations in each
iteration based on the statistical variances estimated from previous
iterations. Using examples of NetBFE calculations for protein binding
of congeneric ligand series, we demonstrate that NetBFE approaches
the optimal allocation in a small number (≤5) of iterations
and that NetBFE reduces the statistical variance in the BFE estimates
by approximately a factor of 2 when compared to a previously published
and widely used allocation method at the same total computational
cost.

化学生成结合自由能（Alchemical Binding Free Energy, BFE）计算可为虚拟结合亲和力预测提供一种高效且热力学严谨的研究路径。历经数十年的方法学优化与近年来计算机技术的飞速进步，化学生成结合自由能（BFE）计算现已广泛应用于药物发现研究领域。该方法可通过预测候选药物分子与目标生物分子靶点的结合亲和力（以及潜在的针对非预期脱靶靶点的选择性），助力药物候选分子的优先级筛选。然而，此类计算所伴随的统计方差可能会损害预测结果的可靠性，既会干扰候选分子的排序结果，也会给预测准确性的基准评估带来不确定性。本文提出一种计算方法，可大幅提升针对结合于同一受体的一组配体的BFE计算的统计精度：该方法依据本团队前期工作确立并在本文中拓展的最优目标，将计算资源动态分配至不同的BFE计算任务中。我们将该方法命名为网络结合自由能（Network Binding Free Energy, NetBFE），其以迭代方式执行自适应BFE计算，基于前序迭代估算得到的统计方差，在每一轮迭代中重新优化资源分配方案。通过针对同系配体系列与蛋白质结合的NetBFE计算实例，我们证实：NetBFE可在少量（≤5次）迭代中趋近最优分配方案，且在相同总计算成本下，与此前已发表并广泛使用的分配方法相比，NetBFE可将BFE估算值的统计方差降低约一半。