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Beyond the cutoff: Hybrid ML/MM electrostatics for neural network potentials.

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Hybrid machine learning/molecular mechanics (ML/MM) neural networks accurately predict binding energies in protein-ligand complexes. This approach enhances scalability and accuracy for complex molecular modeling in various applications.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Chemistry
  • Biophysics

Background:

  • Atomistic Neural Network Potentials (NNPs) offer scalable, low-cost prediction of molecular properties but struggle with long-range interactions.
  • Existing NNPs are local, limiting their reliability for condensed-phase systems crucial in biology, engineering, and pharmaceuticals.
  • Previous work integrated long-range electrostatic information into NNPs (ANI/MM), drawing parallels to quantum mechanics/molecular mechanics (QM/MM).

Purpose of the Study:

  • To develop and train an ANI/MM neural network for predicting binding energies in protein-ligand complexes.
  • To evaluate the accuracy of the ANI/MM network in predicting forces and its potential for molecular dynamics simulations.
  • To compare the performance of the developed ANI/MM NNP against established classical force fields.

Main Methods:

  • Retraining the ANI NNP to incorporate electrostatic potentials from the molecular environment, creating an embedded ANI/MM model.
  • Training the ANI/MM network specifically on two protein-ligand complexes to predict binding energies.
  • Assessing force prediction accuracy and comparing performance against the ab initio-fitted classical force field Q-Force.

Main Results:

  • The ANI/MM network accurately predicts forces with errors under 1 kcal/mol/Å, enabling geometry optimizations and molecular dynamics.
  • The developed ANI/MM NNP surpasses the performance of the Q-Force field for the studied complexes.
  • The model demonstrates good transferability to new solutes when training data includes relevant structural fragments.

Conclusions:

  • Hybrid ML/MM neural architectures provide a promising pathway for chemically accurate and scalable modeling of intricate molecular systems.
  • The ANI/MM NNP offers a significant advancement for simulating biological, engineering, and pharmaceutical applications.
  • This work highlights the potential of integrating long-range interaction information into neural network potentials for enhanced molecular modeling.