Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

The integration of deep learning-based molecular generation models into drug discovery
has garnered significant attention for its potential to expedite the development process …

BindingDB (bindingdb. org) is a public, web-accessible database of experimentally
measured binding affinities between small molecules and proteins, which supports diverse …

The CACHE challenges are a series of prospective benchmarking exercises to evaluate
progress in the field of computational hit-finding. Here we report the results of the inaugural …

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

Proteolysis-targeting chimeras (PROTACs) are drugs designed to degrade target proteins
via the ubiquitin-proteasome system. With the application of computational biology/chemistry …

Active learning (AL) is a specific instance of sequential experimental design and uses
machine learning to intelligently choose the next data point or batch of molecular structures …

Abstract Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules
are designed, synthesised, and assayed to produce data that in turn are analysed to inform …

Generative models for 3D drug design have gained prominence recently for their potential to
design ligands directly within protein pockets. Current approaches, however, often suffer …

Introduction For the past two decades, virtual screening (VS) has been an efficient hit finding
approach for drug discovery. Today, billions of commercially accessible compounds are …