Single-structure scoring functions have been considered inferior to expensive ensemble free energy methods in predicting protein-ligand affinities. We are revisiting this dogma with the recently developed semiempirical quantum-mechanical (SQM)-based scoring function, SQM2.20, comparing its performance to the standard scoring functions on one hand and state-of-the-art molecular dynamics (MD)-based free-energy methods on the other hand. The comparison is conducted on a well-established Wang data set comprising eight protein targets with 200 ligands. The initial low correlation of SQM2.20 scores with the experimental binding affinities of R2 = 0.21 was improved to R2 = 0.47 by a systematic refinement of the input structures and omission of the ligand deformation energy. Consequently, SQM2.20 representing accurate single-structure scoring functions, exhibited an average performance comparable to that of MD-based methods (R2 = 0.52) and surpassed the performance of standard scoring functions (R2 = 0.26). The per-target analysis highlighted the pivotal role of high-quality input structures on the outcomes of single-structure methods. In the instances where such structures are available, SQM2.20 scoring has been shown to rival or even exceed MD-based methods in predicting protein-ligand binding affinities, while exhibiting significantly reduced computation time.

Institute of Organic Chemistry and Biochemistry of The Czech Academy of Sciences 160 00 Prague Czech Republic

Pfizer Worldwide Research and Development Oncology Medicinal Chemistry Pfizer La Jolla California 92121 United States

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