Lab study findings increase understanding of the molecular mechanisms underpinning vitamin D deficiency and highlight SDR42E1 ...

A stroke drug called GAI-17 protected mouse brains even six hours after damage began—and may one day help fight Alzheimer’s ...

Zhuoran Qiao has been awarded the inaugural Chen Institute and Science Prize for Al Accelerated Research for work critical to ...

In April 2025, the US Food and Drug Administration (FDA) published a roadmap for leveraging new approach methodologies (NAMs), including in silico approaches such as artificial intelligence (AI), to ...

Drug discovery has long been criticized for its slow, costly, and failure-prone nature. Traditional approaches, particularly ...

Traditionally, drug discovery relied heavily on trial and error, with long timelines and high costs. The introduction of ...

In an industry where 90 percent of drug candidates fail before reaching the market, a handful of startups are betting ...

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational ...

1 Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, D.C., United States 2 Artificial Intelligence and Drug Discovery (AIDD) Core Laboratory for District of ...

This is particularly important to drug discovery. "When ligands bind to proteins, they kick out water from binding sites, so we need to pay attention to them in ligand design," said Fischer.

To overcome the pitfalls of sample size and dimensionality, this study employed variational autoencoder (VAE), which is a dynamic framework for unsupervised learning in recent years.

Drug discovery has traditionally been slow, expensive and prone to failure, but AI and machine learning are set to change all that.