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 …

Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

Abstract The Biological General Repository for Interaction Datasets (BioGRID:
https://thebiogrid. org) is an open access database dedicated to the curation and archival …

Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the
host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics …

We have designed and developed a data integration and visualization platform that provides
evidence about the association of known and potential drug targets with diseases. The …

Identification of drug–target interactions is an important process in drug discovery. Although
high-throughput screening and other biological assays are becoming available …

Motivation Computational approaches for predicting drug–target interactions (DTIs) can
provide valuable insights into the drug mechanism of action. DTI predictions can help to …

Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key
roles in the discovery/development of drugs, pesticides, food additives, consumer products …