Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …

In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

Computational methods constitute efficient strategies for screening and optimizing potential
drug molecules. A critical factor in this process is the binding affinity between candidate …

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics
(MM), with which one can simulate a biomolecular system efficiently enough and …

The design of biosequences for biosensing and therapeutics is a challenging multistep
search and optimization task. In principle, computational modeling may speed up the design …

While relative binding free energy (RBFE) calculations using alchemical methods are
routinely carried out for many pharmaceutically relevant protein targets, challenges remain …

We present a comprehensive study investigating the potential gain in accuracy for
calculating absolute solvation free energies (ASFE) using a neural network potential to …

Despite a Cambrian explosion in therapeutic modalities, small-molecule drugs remain a
prominent and advantageous medical intervention. The universe of synthesizable, drug-like …

Binding affinity optimization is crucial in early-stage drug discovery. While numerous
machine learning methods exist for predicting ligand potency, their comparative efficacy …

Free energies play a central role in characterising the behaviour of chemical systems and
are among the most important quantities that can be calculated by molecular dynamics …