Target identification and assessment in the era of AI
Target identification and assessment in the era of AI
https://www.nature.com/articles/s41573-026-01412-8
Publish Date: 2026-04-20 04:42:00
Source Domain: www.nature.com
- Druggable genome research and target identification have significantly advanced therapeutic target validation for drug development.
- Multimodal data integration from genomics, proteomics, and other omics fields has become essential for identifying and validating new drug targets.
- The role of artificial intelligence in drug discovery, especially through machine learning and deep learning, has revolutionized target identification and validation.
- Advances in synthetic biology and data generation techniques, such as generative adversarial networks, are enhancing drug discovery by providing realistic synthetic data for training and validation.
- AI-driven computational frameworks are increasingly being used to predict drug interactions and potential adverse effects, improving the safety profile of new therapeutics.
- Recent breakthroughs in protein structure prediction using tools like AlphaFold have led to more accurate drug design and improved drug-target interactions.
- Integration of clinical, pharmacological, and genomic data is essential for the successful identification of novel therapeutic targets and repurposing existing drugs.
- Explainability and transparency in AI-driven drug discovery are critical for building trust and facilitating regulatory approval processes.
