The accuracy of the computational estimation of relative free energies (e.g., for solvation or protein–ligand binding) depends on the smoothness of the phase-space transformation between the two alchemical end-states. A smooth transformation ensures sufficient phase-space overlap between the neighboring intermediate states connecting the two end-states in equilibrium (EQ) simulations and generates less dissipative work in nonequilibrium (NEQ) simulations. The conventional energy interpolation (EI) coupling scheme constructs the intermediate states by linearly combining the end-state potentials. We show that the enveloping distribution sampling (EDS) coupling scheme, a generalization of EI where the corresponding Boltzmann factors are linearly combined, represents a much more flexible alternative. Through the use of a negative smoothing parameter, the EDS scheme increases the local curvature of the sampling phase space along the transformation axis, thereby avoiding phase transitions and creating a smoother transformation. We validate this behavior in increasingly complex settings, from harmonic oscillators and Ising model systems to absolute hydration free-energy (AHFE) calculations on the FreeSolv data set. EDS consistently yields more accurate and statistically robust free-energy estimates compared to the conventional EI scheme for the model system calculations, while a clear advantage is observed for AHFE in the NEQ regime, where less dissipative transitions lead to more reliable free-energy estimates.
Shu-Yu Chen, Enrico Ruijsenaars, P. Hünenberger et al.· Journal of Chemical Theory a...· 0 citations
This work lays the foundation for NNPs where solvation is an integral part of the model, enabling the development of multiscale NNPs for simulating large biomolecular systems.
Moritz Thürlemann, Felix Pultar, Igor Gordiy et al.· Scientific Data· 0 citations
Compared to conventional expectations, it is found that simpler models with interpretable features can achieve competitive performance under rigorous validation protocols and should become a requirement for future ML studies for reaction-yield prediction.
Idil Ismail, Gregory A Landrum, Sereina Riniker· Journal of the American Chem...· 0 citations
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