MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery
Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner, Alex Golts, Jeffrey K Weber, Ella Barkan, Simona Rabinovici-Cohen, Sagi Polaczek, Ido Amos, Ben Shapira, Liam Hazan, Matan Ninio, Sivan Ravid, Michael M Danziger, Yosi Shamay, Sharon Kurant, Joseph A Morrone, Parthasarathy Suryanarayanan · 2026
Read MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery by Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner, Alex Golts, Jeffrey K Weber, Ella Barkan, Simona Rabinovici-Cohen, Sagi Polaczek, Ido Amos, Ben Shapira, Liam Hazan, Matan Ninio, Sivan Ravid, Michael M Danziger, Yosi Shamay, Sharon Kurant, Joseph A Morrone, Parthasarathy Suryanarayanan (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-13267068.txt.
Description
Yoel Shoshan leads a large team presenting a foundation AI model for drug discovery, in this twenty twenty-six paper. Modern AI could speed the search for new drugs, but medical data come in many unlike forms: protein codes, molecule shapes, and gene activity. Models that learn from all of them at once are rare.
Their model, called MAMMAL, was trained on two billion samples across those data types. It can classify, predict numbers, and even generate new candidates from mixed inputs. Tests across many tasks checked how well the shared training paid off. The work aims to give drug hunters one base model for many jobs.
Catalog record
| CREATOR | Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner, Alex Golts, Jeffrey K Weber, Ella Barkan, Simona Rabinovici-Cohen, Sagi Polaczek, Ido Amos, Ben Shapira, Liam Hazan, Matan Ninio, Sivan Ravid, Michael M Danziger, Yosi Shamay, Sharon Kurant, Joseph A Morrone, Parthasarathy Suryanarayanan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |