AI-driven drug–target interaction prediction: current progress, challenges, and future roadmap for precision medicine

2026/03/30 by Hiranmoy Mondal, Amlan Bishal, Biplab Debnath +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Biomedical Text Mining and Ontologies #Computational Drug Discovery Methods

paper · doi:10.1007/s10822-026-00798-2

openalex publication_date 2026/03/30 · crossref created 2026/03/30 · crossref deposited 2026/03/30 · crossref issued 2026/03/31 · crossref published 2026/03/31 · crossref published-online 2026/03/31 · openalex created_date 2026/03/31 · crossref indexed 2026/07/03 · openalex updated_date 2026/07/29

Citations

• RNA Drugs and RNA Targets for Small Molecules: Principles, Progress, and Challenges
• Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
• Graph neural networks: A review of methods and applications
• Highly accurate protein structure prediction with AlphaFold
• Targeting PI3K/Akt signal transduction for cancer therapy
• Computational advances in discovering cryptic pockets for drug discovery
• Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease
• Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Discussions

• Paper: https://doi.org/10.1007/s10822-026-00798-2 Newsletter: https://recep2244.github.io/portfolio/newsletter/2026-06-02-issue-121/ [bsky, 0 points, 0 comments]