Accurate Binding Free Energy Method from End-State MD Simulations

Abstract

Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand–protein and ligand–solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein–ligand complexes show that the method can be accurate and have a correlation of R = 0.87–0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at .

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph

“…Accurate prediction of protein-ligand binding affinity remains one of the grand challenges of computational chemistry and biology. [1][2][3] With the ever increasing amount of high-resolution experimentally determined protein-ligand structures, 4 the binding affinity prediction methods have switched from physicsbased [5][6][7][8][9][10][11] to empirical scoring functions [12][13][14][15] and knowledgebased, 16,17 and in the last decade to machine learning [18][19][20][21][22][23][24][25][26][27] and deep learning based methods. [28][29][30][31][32][33][34][35][36][37][38] Especially, deep learning is an end-to-end method that is ideally suited to find hidden nonlinear relationships 39 between 3D protein-ligand complex structures and binding affinity.…”

The performance of ANI‐ML potentials for ligand‐n(H2O) interaction energies and estimation of hydration free energies from end‐point MD simulations

“…Our custom script (deepQM), an extension to our previous work, 38 to do free energy calculations from ANI applied as post-MD simulations, is written in Python 3.9 and uses ASE libraries to calculate ANI singlepoint energies of the given A-B complex structure (e.g., A = ligand, L; B = solvent, S). As an end-to-end process with the DFT [39][40][41] accuracy, solvation free energy is predicted from LS complex, free ligand and solvent (water) structures, all of which are extracted from the trajectories of classical MD simulations of aqueous ligand system.…”

Applications of Free-Energy Calculations to Biomolecular Processes. A Collection

“…15 Finally, a new strategy from Akkus et al to estimate binding free energies using end-state molecular dynamics simulation based on LIE and ANI-2x neural network potentials predicts the single-point interaction energies between ligand−protein and ligand− solvent pairs at the accuracy of the wb97x/6-31G* level. 16…”

“…In another study by Gusev et al, it was demonstrated that the combination of active learning with automated machine learning and free-energy calculations yields at least 20-fold speedup relative to naïve brute force approaches . Finally, a new strategy from Akkus et al to estimate binding free energies using end-state molecular dynamics simulation based on LIE and ANI-2x neural network potentials predicts the single-point interaction energies between ligand–protein and ligand–solvent pairs at the accuracy of the wb97x/6-31G* level …”