Toward Transferable Deep Learning Atomistic Potential for Biomolecular Simulations

In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension. The new model, dubbed ANI-2x, is trained to sulfur and halogens. These new features open a wide range of new applications within organic chemistry and drug development. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10⁶ factor speedup. A resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.