A Multi-task Large Reasoning Model for Molecular Science

2026-03-13

AI Summary

This work addresses the limited generalizability and insufficient integration of scientific reasoning in existing AI models for molecular science, which often operate as closed systems. To overcome these limitations, the authors propose the first large-scale multi-task reasoning model that explicitly fuses molecular knowledge with structured reasoning, incorporating reflection mechanisms and a mixture-of-experts architecture to emulate scientific cognition. The model leverages knowledge-enhanced chain-of-thought prompting and reinforcement learning to achieve both high efficiency and interpretability in molecular reasoning. Evaluated across 10 molecular tasks and 47 metrics, it achieves an average improvement of 50.3% over more than 20 state-of-the-art baselines. Furthermore, its successful application to central nervous system drug candidate design demonstrates the advantages of smaller-scale, interpretable models in real-world scientific discovery.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• molecular intelligence
• generalizability
• knowledge-guided reasoning
• multi-task learning
• computational molecular design

Innovation

• multi-task reasoning
• chain-of-thought
• molecular knowledge integration
• reinforcement learning
• interpretable AI