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 …

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 …

Highlights•Artificial intelligence (AI) is transforming the drug discovery process by providing
actionable insights from huge amount of data.•Deep-learning models, especially generative …

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

Deep generative models of molecules have grown immensely in popularity, trained on
relevant datasets, these models are used to search through chemical space. The …

Generative deep learning is accelerating de novo drug design, by allowing the generation of
molecules with desired properties on demand. Chemical language models–which generate …

Generative deep learning is resha** drug design. Chemical language models (CLMs)–
which generate molecules in the form of molecular strings–bear particular promise for this …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …