A Multi-task Large Reasoning Model for Molecular Science

TL;DR

This paper presents a multi-task large reasoning model for molecular science that integrates scientific logic with deep learning, achieving significant improvements in molecular tasks and demonstrating practical utility in drug design.

Contribution

The paper introduces a novel multi-task reasoning model with specialized modules and a chain-of-thought framework, enhancing molecular understanding and outperforming large models with less data and resources.

Findings

50.3% average improvement over base architecture

Outperforms 20+ state-of-the-art baselines

Effective in CNS drug candidate design

Abstract

Advancements in artificial intelligence for molecular science are necessitating a paradigm shift from purely data-driven predictions to knowledge-guided computational reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. This underscores the necessity for computational methods that can effectively integrate scientific logic with deep learning architectures. Here we introduce a multi-task large reasoning model designed to emulate the cognitive processes of molecular scientists through structured reasoning and reflection. Our approach incorporates multi-specialist modules to provide versatile molecular expertise and a chain-of-thought (CoT) framework enhanced by reinforcement learning infused with molecular knowledge, enabling structured and reflective reasoning. Systematic evaluations across 10 molecular tasks and 47 metrics demonstrate that our model achieves an average 50.3% improvement over the base architecture, outperforming over 20 state-of-the-art baselines, including ultra-large-parameter foundation models, despite using significantly fewer training data and computational resources. This validates that embedding explicit reasoning mechanisms enables high-efficiency learning, allowing smaller-scale models to surpass massive counterparts in both efficacy and interpretability. The practical utility of this computational framework was validated through a case study on the design of central nervous system (CNS) drug candidates, illustrating its capacity to bridge data-driven and knowledge-integrated approaches for intelligent molecular design.