分子相互作用基础模型 Bonbon 展现零样本涌现生物学能力

论文在 42M 到 1.6B 共六个参数规模上做了对照缩放实验：预训练损失平滑下降，但所有涌现能力都在最大规模上出现跳变，说明基于损失的缩放定律无法预测零样本能力何时出现。训练采用 1:2000 的参数- token 比以适应配对生物数据的稀疏性。

同一冻结表示在结合与亲和力预测上达到 SOTA，速度比共折叠方法快五个数量级。前瞻性湿实验覆盖 7 个蛋白家族的 9 个疾病靶点，从头设计化合物的剂量响应命中率 61.5%，其中包含此前未见报道的全新抑制剂。

Emergent Biological Capabilities in a Foundation Model for Molecular Interactions

Foundation models trained on large unlabeled corpora develop emergent capabilities, tasks that go beyond the training objective. We extend this paradigm to multimodal biology with Bonbon, a foundation model for molecular interactions trained on approximately 3.2 trillion tokens of paired protein-ligand sequences. From a single representation space, Bonbon exhibits three zero-shot emergent capabilities at hierarchical levels of resolution: functional (mechanism of action), residue (binding site localization, including orthosteric/allosteric discrimination), and bond (covalent versus non-covalent engagement). All three capabilities generalize to out-of-distribution complexes. Bonbon was trained on protein-ligand sequences alone, without any task-specific supervision and without structural coordinates. We conducted controlled scaling experiments across six model scales from 42M to 1.6B parameters; every capability evaluated shows a sharp jump at the largest scale on continuous metrics, even as pretraining loss decreases smoothly. This demonstrates that loss-based scaling laws alone are insufficient to predict when zero-shot emergent capabilities become available. The capabilities reported here emerge after training at a 1:2,000 parameter-to-token ratio to accommodate the sparsity of paired biological interaction data. The same frozen representations achieve state-of-the-art binding and affinity prediction at five orders of magnitude greater speed than co-folding methods. In prospective wet-lab validation across nine disease targets spanning seven protein families, Bonbon achieves a 61.5% dose-response hit rate on de novo compounds, yielding structurally novel molecules not previously reported to inhibit these proteins, including for out-of-distribution targets.

This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Bonbon, 分子相互作用, 缩放定律, 药物发现.

Source attribution: curated from arXiv cs.LG (@Adi Mashiach).