Solvation Free Energies of Drug-like Molecules via Fast Growth in an Explicit Solvent: Assessment of the AM1-BCC, RESP/HF/6-31G*, RESP-QM/MM, and ABCG2 Fixed-Charge Approaches
Abstract
We assess the performance of the nonequilibrium alchemical fast-growth method in calculating water and 1-octanol solvation free energies, comparing the recently proposed ABCG2 model with other empirical and quantum mechanics (QM)-based approaches for modeling electrostatic interactions in condensed phases using fixed atomic charges. The fixed-charge protocols are tested on the challenging set of drug-like polyfunctional molecules previously used by Vassetti et al., J. Chem. Theory Comput. 2019, 15, 1983-1995, broadly spanning the chemical space and often exhibiting complex conformational landscapes. We find that the cost-effective empirical ABCG2 protocol consistently outperforms the AM1/BCC precursor model and the widely used HF/6-31G* ab initio charge derivation method, achieving solvation free energy accuracy comparable to an expensive QM/MM-based methodology for atomic fixed charge determination. For water-octanol transfer free energies, ABCG2 benefits from systematic error cancellation, yielding remarkable agreement with experimental data, exhibiting excellent Pearson and Kendall rank coefficients and a mean unsigned error below 1 kcal/mol, matching the performance of the costly QM/MM approach. These results suggest that the ABCG2 protocol holds great promise for the high-throughput in silico prediction of ligand-protein binding free energies in drug discovery projects.
How strong is the evidence?
Evidence Hub reads this as a experimental / empirical study, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.