R&D Center, PharmCADD, Busan 48060, Republic of Korea.
Department of Physics, Pukyong National University, Busan 48513, Republic of Korea.
Curr Drug Metab. 2022;23(4):252-259. doi: 10.2174/1389200223666220315160835.
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of research, along with the complexity of the algorithm, time-consuming procedures, and reproducibility issues. In this review, we have discussed the advantages and disadvantages of diverse free energy methods like Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), and alchemical methods. Moreover, we discussed the possible application of the machine learning method in proteinligand binding free energy estimation.
估算候选药物与生物分子靶标之间的结合自由能是计算机辅助药物发现中最好的定量估算方法之一。尽管经过几十年的研究,算法的复杂性、耗时的程序和可重复性问题仍然使得准确估算结合自由能具有挑战性。在这篇综述中,我们讨论了各种自由能方法的优缺点,如热力学积分 (TI)、Bennett 的接受比 (BAR)、自由能微扰 (FEP) 和化学计量方法。此外,我们还讨论了机器学习方法在蛋白质配体结合自由能估算中的可能应用。