Free energies at QM accuracy from force fields via multimap targeted estimation

Proceedings of the National Academy of Sciences, vol. 120, pp. e2304308120

Abstract

Accurate predictions of ligand binding affinities would greatly accelerate the first stages of drug discovery campaigns. However, using highly accurate interatomic potentials based on quantum mechanics (QM) in free energy methods has been so far largely unfeasible due to their prohibitive computational cost. Here, we present an efficient method to compute QM free energies from simulations using cheap reference potentials, such as force fields (FFs). This task has traditionally been out of reach due to the slow convergence of computing the correction from the FF to the QM potential. To overcome this bottleneck, we generalize targeted free energy methods to employ multiple maps-implemented with normalizing flow neural networks (NNs)-that maximize the overlap between the distributions. Critically, the method requires neither a separate expensive training phase for the NNs nor samples from the QM potential. We further propose a one-epoch learning policy to efficiently avoid overfitting, and we combine our approach with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. On the drug-like molecules in the HiPen dataset, the method accelerates the calculation of the free energy difference of switching from an FF to a DFTB3 potential by three orders of magnitude compared to standard free energy perturbation and by a factor of eight compared to previously published nonequilibrium calculations. Our results suggest that our method, in combination with efficient QM/MM calculations, may be used in lead optimization campaigns in drug discovery and to study protein-ligand molecular recognition processes.

Authors 3

1. Andrea Rizzi

   Forschungszentrum Jülich · Italian Institute of Technology

   Affiliation as printed

   Atomistic Simulations, Italian Institute of Technology, Genova 16163, Italy

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

   Atomistic Simulations, Italian Institute of Technology, Via Morego 30, Genova 16163, Italy

2. Paolo Carloni Aachen Department of Physics and

   RWTH Aachen University · Universitätsklinikum Aachen · Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

   Department of Physics and Universitätsklinikum, RWTH Aachen University, Aachen 52074, Germany

3. Michele Parrinello

   Italian Institute of Technology

   Affiliation as printed

   Atomistic Simulations, Italian Institute of Technology, Genova 16163, Italy

   Atomistic Simulations, Italian Institute of Technology, Via Morego 30, Genova 16163, Italy