Emerging computational tools promise to revolutionize protein engineering for biocatalytic
applications and accelerate the development timelines previously needed to optimize an …

Abstract Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of various ML …

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of
incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic …

Drug discovery pipelines nowadays rely on machine learning models to explore and
evaluate large chemical spaces. While including 3D structural information is considered …

Accurate prediction of protein‐ligand binding affinities is an essential challenge in structure‐
based drug design. Despite recent advances in data‐driven methods for affinity prediction …

In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a
powerful tool for predicting binding affinities, estimating transport properties, and exploring …

We introduce a framework for the use of large language models (LLMs) in Building
Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving …

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of
incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic …

We introduce In-Pocket Analysis, a simple and efficient protein-ligand complex structure
optimization algorithm. It provides structural biology and structure-based drug discovery with …

Molecular dynamics (MD) simulations of protein-ligand complexes are essential for
computer-aided drug design. In particular they enable the calculation of free energies and …