Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal force fields that can reliably model biological and drug-like molecules, including alternative tautomers and protonation states. Herein, we compare the performance of several neglect of diatomic differential overlap-based semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density-functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and GFN2-xTB) models with pure machine learning potentials (ANI-1x and ANI-2x) and hybrid quantum mechanical/machine learning potentials (AIQM1 and QD pi) for a wide range of data computed at a consistent omega B97X/6-31G* level of theory (as in the ANI-1x database). This data includes conformational energies, intermolecular interactions, tautomers, and protonation states. Additional comparisons are made to a set of natural and synthetic nucleic acids from the artificially expanded genetic information system that has important implications for the design of new biotechnology and therapeutics. Finally, we examine the acid/base chemistry relevant for RNA cleavage reactions catalyzed by small nucleolytic ribozymes, DNAzymes, and ribonucleases. Overall, the hybrid quantum mechanical/machine learning potentials appear to be the most robust for these datasets, and the recently developed QD pi model performs exceptionally well, having especially high accuracy for tautomers and protonation states relevant to drug discovery.

Automated summary

Modern semiempirical electronic structure methods have potential applications in drug discovery for accurately modeling biological and drug-like molecules. Comparisons were made between different methods, and the hybrid quantum mechanical/machine learning potentials, especially the QD pi model, showed the most robust performance for tautomers and protonation states relevant to drug discovery.