Cross-docking and redocking reveal distinct determinants of success in physics-based and AI-driven binding pose prediction in protein–ligand complexes
Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan · 2026
Read Cross-docking and redocking reveal distinct determinants of success in physics-based and AI-driven binding pose prediction in protein–ligand complexes by Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13551558.txt.
Description
Suri and coauthors compare old and new computer tools that guess drug fits. Drug design needs the right pose of a drug in a protein pocket. Classic tools use physics rules to score each pose. New tools learn poses from large sets of past data.
The team tested both kinds on three brain illness targets. Each target has a pocket shape quite unlike the rest. Classic tools did well when the true pose was known. Their scores fell more in hard cross-tests with new pockets. Learning tools held up in some of those tests. The study maps when each kind works best.
Catalog record
| CREATOR | Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 85 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |