Faver John C, Ucisik M Nihan, Yang Wei, Merz Kenneth M
Department of Chemistry and the Quantum Theory Project, 2328 New Physics Building, P.O. Box 118435, University of Florida, Gainesville, Florida 32611-8435.
ACS Med Chem Lett. 2013 Sep 12;4(9):812-4. doi: 10.1021/ml4002634.
Computer-aided drug design could benefit from a greater understanding of how errors arise and propagate in biomolecular modeling. With such knowledge, model predictions could be associated with quantitative estimates of their uncertainty. In addition, novel algorithms could be designed to proactively reduce prediction errors. We investigated how errors propagate in statistical mechanical ensembles and found that free energy evaluations based on single molecular configurations yield maximum uncertainties in free energy. Furthermore, increasing the size of the ensemble by sampling and averaging over additional independent configurations reduces uncertainties in free energy dramatically. This finding suggests a general strategy that could be utilized as a post-hoc correction for improved precision in virtual screening and free energy estimation.
计算机辅助药物设计若能更深入地理解生物分子建模中错误是如何产生和传播的，将从中受益。有了这些知识，模型预测结果就能与对其不确定性的定量估计相关联。此外，还可以设计新的算法来主动减少预测误差。我们研究了错误在统计力学系综中的传播方式，发现基于单个分子构型的自由能评估会在自由能中产生最大的不确定性。此外，通过对额外的独立构型进行采样和平均来增加系综的规模，能显著降低自由能的不确定性。这一发现提出了一种通用策略，可作为事后校正方法，用于提高虚拟筛选和自由能估计的精度。
