Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment
arXiv:2606.18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token prediction, providing the likelihood interface central to sequence design, variant scoring, and mechanistic interpretation. Yet these distributions are learned from broad unlabeled corpora and are not naturally conditioned on task-specific biological contexts such as interaction partners, cellular environments, or therapeutic interventions. Existing contextual matching methods often distort this interface through pooled embeddings, contrastive la
The growing sophistication of biological language models necessitates methods to contextualize their outputs for specific biological tasks, moving beyond broad corpus training.
Improving the contextual relevance of biological language models directly impacts their utility in drug discovery, protein engineering, and understanding cellular mechanisms, accelerating the pace of synthetic biology.
Biological language models can now be trained to better incorporate specific biological contexts, making their predictions more accurate and actionable for targeted applications.
- · Biopharmaceutical companies
- · Synthetic biology researchers
- · AI-driven drug discovery platforms
- · Biotech startups
- · Traditional wet-lab experimental methods (displacement)
- · Companies reliant on less precise biological modeling
More accurate and efficient development of novel biologics and therapies due to advanced predictive models.
Accelerated discovery cycles in areas like antibody design and enzyme engineering, leading to new markets and products.
The blurring of lines between in-silico and in-vitro experimentation, potentially leading to fully autonomous biological design pipelines.
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Read at arXiv cs.LG