MolTC: Towards Molecular Relational Modeling In Language Models (2402.03781v6)

Abstract: Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of LLMs, known for their vast knowledge repositories and advanced logical inference capabilities, has emerged as a promising way for efficient and effective MRL. Despite their potential, these methods predominantly rely on the textual data, thus not fully harnessing the wealth of structural information inherent in molecular graphs. Moreover, the absence of a unified framework exacerbates the issue of information underutilization, as it hinders the sharing of interaction mechanism learned across diverse datasets. To address these challenges, this work proposes a novel LLM-based multi-modal framework for Molecular inTeraction prediction following Chain-of-Thought (CoT) theory, termed MolTC, which effectively integrate graphical information of two molecules in pair. To train MolTC efficiently, we introduce a Multi-hierarchical CoT concept to refine its training paradigm, and conduct a comprehensive Molecular Interactive Instructions dataset for the development of biochemical LLMs involving MRL. Our experiments, conducted across various datasets involving over 4,000,000 molecular pairs, exhibit the superiority of our method over current GNN and LLM-based baselines. Code is available at https://github.com/MangoKiller/MolTC.

• The paper presents a novel LLM-based multi-modal framework that leverages both textual and structural data to enhance molecular relational learning.
• It fuses graph neural networks with a dynamic parameter-sharing strategy to effectively encode molecular graphs and mitigate data scarcity challenges.
• The multi-hierarchical chain-of-thought training refines predictions from qualitative assessments to precise quantitative outcomes across diverse datasets.

Analysis of MolTC: A Multi-Modal Framework for Molecular Relational Learning

The paper "MolTC: Towards Molecular Relational Modeling In LLMs" presents a significant advancement in the field of molecular relational learning (MRL) by developing a novel LLM-based multi-modal framework, termed MolTC, which aims to effectively predict interactions between molecular pairs. This work addresses critical limitations in the current methodologies for MRL by introducing an approach that leverages both textual and structural data inherent in molecular graphs, setting it apart from earlier practices that primarily relied on textual data.

Central to the MolTC framework is the integration of Graph Neural Networks (GNNs) and LLMs in a coherent manner that respects the intrinsic graphical nature of molecular data. By employing GNNs, MolTC efficiently encodes molecular graphs, thus harnessing the rich structural information critical for accurate interaction prediction. The framework is further bolstered by a Dynamic Parameter-sharing Strategy that facilitates cross-dataset information exchange, a capability that ensures seamless learning across diverse datasets without the typical drawbacks of overfitting associated with limited samples.

A noteworthy aspect of the MolTC framework is its training paradigm, which is underpinned by the Multi-hierarchical Chain-of-Thought (CoT) theory. This innovative approach involves a two-fold training strategy: initially, a Broad-grained CoT is employed during pretraining to focus on individual molecular properties, thereby laying the groundwork for accurately assessing interactions. Subsequently, during fine-tuning, a Fine-grained CoT prompts a gradual refinement process, starting from a range prediction to a precise numerical interaction outcome. This systematic refinement is particularly beneficial for tasks requiring quantitative assessments, common in MRL applications like solute-solvent interactions (SSI).

The paper empirically validates the superiority of MolTC across a suite of experiments involving over 4,000,000 molecular pairs spread across twelve datasets. In these evaluations, MolTC consistently outperforms both GNN-based and LLM-based baselines by a significant margin. This performance is measured against key metrics such as accuracy, AUC-ROC for qualitative tasks, and MAE and RMSE for quantitative ones. The results underline the effectiveness of the MolTC framework in mitigating the common pitfalls of previous methodologies, particularly its ability to draw meaningful relational insights from both molecular sequences and structured data.

Furthermore, the paper introduces MoT-instructions, a comprehensive dataset that serves to enhance the development of biochemical LLMs involved in MRL. By intricately curating molecular pairs with detailed instructions derived from diverse molecular interaction datasets, MoT-instructions stands to serve as a critical asset for ongoing and future developments in the field.

The potential implications of the MolTC model are considerable, particularly in expediting drug discovery and chemical process design where understanding molecular interactions is paramount. The integration of a unified training framework not only streamlines model deployment across tasks but also enriches the repository of biochemical insights accessible to the research community.

Looking ahead, the potential for MolTC to adapt and apply within contexts demanding few-shot or zero-shot learning is an area ripe for exploration. Moreover, future research might also consider optimizing projector designs or incorporating real-time biochemical feedback to further enhance model robustness and efficiency. This prospect opens up exciting avenues for furthering the reach and impact of LLMs within chemical and biological sciences.