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 …

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

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 …

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 …

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 …

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 …

Recent research on artificial intelligence indicates that machine learning algorithms can
auto-generate novel drug-like molecules. Generative models have revolutionized de novo …

Generative chemical language models (CLMs) can be used for de novo molecular structure
generation by learning from a textual representation of molecules. Here, we show that hybrid …