DTA Atlas: A massive-scale drug repurposing database

Abstract

The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected drug-target interactions across the entire human proteome is still lacking. In this work, we introduce an open database of predicted drug-target interactions, termed DTA Atlas, covering the entire human proteome as well as a wide range of marketed drugs, resulting in over 220 million drug-target pairs. The database integrates 4 billion affinity predictions from advanced deep neural networks and offers a user-friendly web interface, enabling users to explore drug-target affinity predictions for the human proteome. To the best of our knowledge, DTA Atlas represents the first comprehensive collection of drug-target binding strength predictions. It is open-source and can serve as an important resource for drug development, drug repurposing, toxicity studies and more.

Keywords

• Drug discovery
• Drug repurposing database
• Machine learning
• Drug target affinity prediction
• Drugbank
• Uniprot