SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules

TL;DR

SciCore-Mol enhances large language models with specialized modules for molecular perception, generation, and reasoning, significantly improving performance on chemical tasks and enabling scientific discovery.

Contribution

It introduces a modular framework with pluggable cognitive modules that bridge the gap between linguistic and molecular data in LLMs, advancing scientific AI capabilities.

Findings

Achieves strong performance across chemical tasks

Surpasses some proprietary models in several dimensions

Provides a systematic blueprint for scientific LLMs

Abstract

Large Language Models (LLMs) are central to the one-for-all intelligent paradigm, but they face a fundamental challenge when dealing with heterogeneous scientific data such as molecules: the inherent gap between discrete linguistic symbols and topological molecular or continuous reaction data leads to significant information loss and semantic noise in text-based reasoning. We propose SciCore-Mol, a modular framework that bridges this gap through three deeply integrated pluggable cognitive modules: a topology-aware perception module, a latent diffusion-based molecular generation module, and a reaction-aware reasoning module. Each module is coupled to the LLM backbone through learned representation interfaces, enabling richer information exchange than is possible with text-only tool feedback. Our experiments on diverse chemical tasks demonstrate that SciCore-Mol achieves strong comprehensive performance across molecular understanding, generation, reaction prediction, and general chemistry knowledge, with an 8B-parameter open-source system that is competitive with and in several dimensions surpasses proprietary large models. This work provides a systematic blueprint for equipping LLMs with scientific expertise through decoupled, pluggable, and flexibly orchestrated modules, with direct implications for drug design, chemical synthesis, and broader scientific discovery.