Machine Learning Enhances Bio macromolecular Modeling With Quantum Chemistry

The article discusses a machine learning force field for biomacromolecular modeling based on quantum chemistry-calculated interaction energy datasets. The authors, ZhenXuan Fan and Sheng D Chao, have used the SAPT2 level of theory to recalculate intermolecular interaction energies. They have then used the CLIFF machine learning scheme to construct a general-purpose force field for biomolecular dynamics simulations. The results show that the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set, with errors well below the desired chemical accuracy of 1 kcal/mol.