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<p>Modern AI (Artificial Intelligence) methods offer new opportunities in pharmacology by enabling improved modeling of disease mechanisms and drug action learned from large and heterogeneous biological datasets. A central challenge is developing models that can jointly integrate disparate biomedical modalities. We introduce <b>MAMMAL</b> (<b>M</b>olecular <b>A</b>ligned <b>M</b>ulti <b>M</b>odal <b>A</b>rchitecture and <b>L</b>anguage), a foundation model for cross-modal learning, designed to address the challenges associated with drug discovery tasks. MAMMAL was pre-trained on 2 billion samples across protein and antibody sequences, small molecules, and gene expression profiles, and supports classification, regression, and generative tasks on cross-modal inputs. Across eleven benchmarks covering multiple stages of the drug discovery pipeline, MAMMAL achieves state-of-the-art performance on nine tasks and competitive results on two. In an antibody-antigen binding benchmark, fine-tuned MAMMAL prediction scores significantly outperform AlphaFold3 confidence scores, used here as a reference proxy for binding likelihood, in five of seven antigen targets. The MAMMAL framework and pretrained models are publicly available to support open and collaborative research.</p>

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MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

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  • 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,
  • Michal Rosen-Zvi,
  • Efrat Hexter

摘要

Modern AI (Artificial Intelligence) methods offer new opportunities in pharmacology by enabling improved modeling of disease mechanisms and drug action learned from large and heterogeneous biological datasets. A central challenge is developing models that can jointly integrate disparate biomedical modalities. We introduce MAMMAL (Molecular Aligned Multi Modal Architecture and Language), a foundation model for cross-modal learning, designed to address the challenges associated with drug discovery tasks. MAMMAL was pre-trained on 2 billion samples across protein and antibody sequences, small molecules, and gene expression profiles, and supports classification, regression, and generative tasks on cross-modal inputs. Across eleven benchmarks covering multiple stages of the drug discovery pipeline, MAMMAL achieves state-of-the-art performance on nine tasks and competitive results on two. In an antibody-antigen binding benchmark, fine-tuned MAMMAL prediction scores significantly outperform AlphaFold3 confidence scores, used here as a reference proxy for binding likelihood, in five of seven antigen targets. The MAMMAL framework and pretrained models are publicly available to support open and collaborative research.