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Optimal designs for pairwise calculation: An application to free energy perturbation in minimizing prediction variability
Author(s) -
Yang Qingyi,
Burchett Woodrow,
Steeno Gregory S.,
Liu Shuai,
Yang Mingjun,
Mobley David L.,
Hou Xinjun
Publication year - 2020
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26095
Subject(s) - pairwise comparison , free energy perturbation , perturbation (astronomy) , mathematics , similarity (geometry) , computer science , algorithm , mathematical optimization , statistics , chemistry , molecular dynamics , computational chemistry , artificial intelligence , physics , quantum mechanics , image (mathematics)
Pairwise‐based methods such as the free energy perturbation (FEP) method have been widely deployed to compute the binding free energy differences between two similar host–guest complexes. The calculated pairwise free energy difference is either directly adopted or transformed to absolute binding free energy for molecule rank ordering. We investigated, through both analytic derivations and simulations, how the selection of pairs in the experiment could impact the overall prediction precision. Our studies showed that (1) the estimated absolute binding free energy ( Δ G ^ ) derived from calculated pairwise differences (ΔΔ G ) through weighted least squares fitting is more precise in prediction than the pairwise difference values when the number of pairs is more than the number of ligands and (2) prediction precision is influenced by both the total number of pairs and the specifically selected pairs, the latter being critically important when the number of calculated pairs is limited. Furthermore, we applied optimal experimental design in pair selection and found that the optimally selected pairs can outperform randomly selected pairs in prediction precision. In an illustrative example, we showed that, upon weighing ligand structure similarity into design optimization, the weighted optimal designs are more efficient than the literature reported designs. This work provides a new approach to assess retrospective pairwise‐based prediction results, and a method to design new prospective pairwise‐based experiments for molecular lead optimization. © 2019 Wiley Periodicals, Inc.