Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …

Abstract Machine learning has provided a means to accelerate early-stage drug discovery
by combining molecule generation and filtering steps in a single architecture that leverages …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Language models have contributed to breakthroughs in interdisciplinary research, such as
protein design and molecular dynamics understanding. In this study, we reveal that beyond …

Drug-discovery and drug-development endeavors are laborious, costly and time consuming.
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …

De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …

Generative machine learning models have attracted intense interest for their ability to
sample novel molecules with desired chemical or biological properties. Among these …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Deep learning-based molecular generation has extensive applications in many fields,
particularly drug discovery. However, the majority of current deep generative models are …

Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …