Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow
Jinming Bai, Jessica Sarah Faure, Tawfeeq Ahmed Khalfe, Rongqin Huang, Carly Ann Burmeister, Annick van Niekerk, Sharon Prince · 2026
Read Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow by Jinming Bai, Jessica Sarah Faure, Tawfeeq Ahmed Khalfe, Rongqin Huang, Carly Ann Burmeister, Annick van Niekerk, Sharon Prince (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-13583913.txt.
Description
Jinming Bai and six co-authors review how artificial intelligence can link the full path of cancer drug discovery. In most work each AI tool is used for one step alone. The review instead treats drug work as four linked stages, from finding a target to testing drug response and resistance.
The authors propose a design, test, and refine loop that spans all four stages. In that loop, AI predictions guide lab tests, and test results then improve the next round of design. They stress that AI should guide lab and clinical judgment, not replace it. Weak training data, tumor diversity, and poor transfer to new cases remain key limits that need strong checks in more complex models.
Catalog record
| CREATOR | Jinming Bai, Jessica Sarah Faure, Tawfeeq Ahmed Khalfe, Rongqin Huang, Carly Ann Burmeister, Annick van Niekerk, Sharon Prince |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |