MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.

Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space.

Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2.

This study introduces a sophisticated computational pipeline, , designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g.

Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases.

The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline.