Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some
protagonists point to vast opportunities potentially offered by such tools, others remain …

The discovery and development of small molecule cancer drugs has been revolutionised
over the last decade. Most notably, we have moved from a one-size-fits-all approach that …

Drug design is central to the discovery of a new medicine, as the drug molecule embodies
the therapeutic hypothesis of a drug discovery program and encodes the properties …

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding
for drug discovery. Using deep learning for molecular design and a microfluidics platform for …

Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late.
Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of …

Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the
generalization of predictive models by leveraging shared information across multiple tasks …

As part of the drug discovery and development process, it is important to understand the fate
of the drug candidate in humans and the relevance of the animal species used for preclinical …

Targeted covalent inhibition is an established approach for increasing the potency and
selectivity of potential drug candidates, as well as identifying potent and selective tool …

Artificial intelligence has brought crucial changes to the whole field of natural sciences.
Myriads of machine learning algorithms have been developed to facilitate the work of …

Screening of large and diverse libraries is the 'bread and butter'in the first phase of the
discovery of novel drugs. However, maintenance and periodic renewal of high-quality large …