OwkinZero: Accelerating Biological Discovery with AI

TL;DR

OwkinZero introduces specialized AI models trained on curated biological datasets that outperform larger models in biological reasoning tasks, advancing AI's role in biomedical discovery.

Contribution

The paper presents a new benchmark dataset collection and a reinforcement learning approach to develop specialized LLMs that excel in biological reasoning tasks.

Findings

OwkinZero models outperform larger commercial LLMs on biological benchmarks.

Specialist models trained on single tasks outperform base models on unseen tasks.

Training on diverse datasets enhances cross-task generalization.

Abstract

While large language models (LLMs) are rapidly advancing scientific research, they continue to struggle with core biological reasoning tasks essential for translational and biomedical discovery. To address this limitation, we created and curated eight comprehensive benchmark datasets comprising over 300,000 verifiable question-and-answer pairs, each targeting critical challenges in drug discovery including target druggability, modality suitability, and drug perturbation effects. Using this resource, we developed the OwkinZero models by post-training open-source LLMs through a Reinforcement Learning from Verifiable Rewards strategy. Our results demonstrate that specialized 8-32B OwkinZero models substantially outperform larger, state-of-the-art commercial LLMs on these biological benchmarks. Remarkably, we uncover evidence of a key aspect of generalization: specialist models trained on a single task consistently outperform their base models on previously unseen tasks. This generalization effect is further amplified in our comprehensive OwkinZero models, which were trained on a mixture of datasets and achieve even broader cross-task improvements. This study represents a significant step toward addressing the biological reasoning blind spot in current LLMs, demonstrating that targeted reinforcement learning on carefully curated data can unlock generalizable performance in specialized models, thereby accelerating AI-driven biological discovery.