Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

In the past, conventional drug discovery strategies have been successfully employed to
develop new drugs, but the process from lead identification to clinical trials takes more than …

Drug discovery relies on the knowledge of not only drugs and targets, but also the
comparative agents and targets. These include poor binders and non-binders for develo** …

Photopharmacology is an attractive approach for achieving targeted drug action with the use
of light. In photopharmacology, molecular photoswitches are introduced into the structure of …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …

Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …

Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …

The lack of standard approaches in microplastic research limits progress in the abatement of
plastic pollution. Here, we propose and test rescaling methods that are able to improve the …