In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

This Perspective provides a contextual explanation of the current state-of-the-art alchemical
free energy methods and their role in drug discovery as well as highlights select emerging …

Hybrid quantum mechanical/molecular mechanical (QM/MM) methods have become
indispensable tools for the study of biomolecules. In this article, we briefly review the basic …

This roadmap reviews the new, highly interdisciplinary research field studying the behavior
of condensed matter systems exposed to radiation. The Review highlights several recent …

We report QDπ-v1. 0 for modeling the internal energy of drug molecules containing H, C, N,
and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning …

We present a fast, accurate, and robust approach for determination of free energy profiles
and kinetic isotope effects for RNA 2′-O-transphosphorylation reactions with inclusion of …

Modern semiempirical electronic structure methods have considerable promise in drug
discovery as universal “force fields” that can reliably model biological and drug-like …

Despite the success and widespread use of QM/MM methods in modeling (bio) chemically
important processes, their accuracy is still not well understood. A key reason is because …

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic
modeling, chemical process design, and high-throughput solvent screening. Despite the …

Free energy simulations that employ combined quantum mechanical and molecular
mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly …