Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

G protein–coupled receptors (GPCRs) constitute the largest family of membrane proteins in
the human genome and are important therapeutic targets. During the last decade, the …

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us …

With the rapid progress of AI in both academia and industry, Deep Learning has been widely
introduced into various areas in drug discovery to accelerate its pace and cut R&D costs …

Despite an abundance of online databases providing access to chemical data, there is
increasing demand for high-quality, structure-curated, open data to meet the various needs …

Molecular machine learning has been maturing rapidly over the last few years. Improved
methods and the presence of larger datasets have enabled machine learning algorithms to …

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

Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …

The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

Molecular “fingerprints” encoding structural information are the workhorse of
cheminformatics and machine learning in drug discovery applications. However, fingerprint …