Title:Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy
Estimation
Volume: 23
Issue: 4
Author(s): Dakshinamurthy Sivakumar and Sangwook Wu*
Affiliation:
- R&D Center, PharmCADD, Busan 48060, Republic of Korea
- Department of Physics, Pukyong National University, Busan 48513,
Republic of Korea
Keywords:
Free energy, Bennett's acceptance ratio (BAR), alchemical methods, machine learning, computer-aided drug discovery, thermodynamic integration (TI).
Abstract: 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.