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 …

Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …

Identifying interactions between known drugs and targets is a major challenge in drug
repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive …

Computational prediction of the interaction between drugs and targets is a standing
challenge in the field of drug discovery. A number of rather accurate predictions were …

Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …

Background Study of drug-target interaction networks is an important topic for drug
development. It is both time-consuming and costly to determine compound-protein …

Motivation: Molecular representation for small molecules has been routinely used in
QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other …

Polypharmacology, which focuses on designing therapeutics to target multiple receptors,
has emerged as a new paradigm in drug discovery. Polypharmacological effects are an …

Multi-target drugs against selective multiple targets improve therapeutic efficacy, safety and
resistance profiles by collective regulations of a primary therapeutic target together with …

Complex diseases (eg, Alzheimer's disease) or infectious diseases are usually caused by
complicated and varied factors, including environmental and genetic factors. Multi-target …