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

Temel M., Tayfuroglu O., Kocak A.

Journal of Computational Chemistry, cilt.44, sa.4, ss.559-569, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 4
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1002/jcc.27022
  • Dergi Adı: Journal of Computational Chemistry
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Chemical Abstracts Core, Chimica, Compendex, EMBASE, INSPEC, MEDLINE
  • Sayfa Sayıları: ss.559-569
  • Anahtar Kelimeler: free energy, interaction energy, machine learning, molecular dynamics, solvation, LIGAND-BINDING, SOLVENT, COMPUTER
  • Kocaeli Üniversitesi Adresli: Hayır

Özet

© 2022 Wiley Periodicals LLC.Here, we investigate the performance of “Accurate NeurAl networK engINe for Molecular Energies” (ANI), trained on small organic compounds, on bulk systems including non-covalent interactions and applicability to estimate solvation (hydration) free energies using the interaction between the ligand and explicit solvent (water) from single-step MD simulations. The method is adopted from ANI using the Atomic Simulation Environment (ASE) and predicts the non-covalent interaction energies at the accuracy of wb97x/6-31G(d) level by a simple linear scaling for the conformations sampled by molecular dynamics (MD) simulations of ligand-n(H2O) systems. For the first time, we test ANI potentials' abilities to reproduce solvation free energies using linear interaction energy (LIE) formulism by modifying the original LIE equation. Our results on ~250 different complexes show that the method can be accurate and have a correlation of R2 = 0.88–0.89 (MAE <1.0 kcal/mol) to the experimental solvation free energies, outperforming current end-state methods. Moreover, it is competitive to other conventional free energy methods such as FEP and BAR with 15-20 × fold reduced computational cost.