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 …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Abstract ChEMBL (https://www. ebi. ac. uk/chembl/) is a manually curated, high-quality, large-
scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like …

The application of computational approaches in drug discovery has been consolidated in
the last decades. These families of techniques are usually grouped under the common …

Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …

Molecular docking predicts whether and how small molecules bind to a macromolecular
target using a suitable 3D structure. Scoring functions for structure‐based virtual screening …

The reproducibility of experiments has been a long standing impediment for further scientific
progress. Computational methods have been instrumental in drug discovery efforts owing to …

The growing quantity of public and private data sets focused on small molecules screened
against biological targets or whole organisms provides a wealth of drug discovery relevant …

The identification of physical interactions between drug candidate compounds and target
biomolecules is an important process in drug discovery. Since conventional screening …

Highlights•Biochemical data can be represented with text-based languages codified by
humans.•Natural language processing (NLP) can be applied to textual biochemical …