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InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery

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arxiv 2311.16208 v2 pith:TP54U4MD submitted 2023-11-27 q-bio.BM cs.AIcs.LG

classification q-bio.BMcs.AIcs.LG
keywords moleculardrugdiscoveryinstructmolassistantdatalanguagellms
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data. Our novel contribution, InstructMol, a multi-modal LLM, effectively aligns molecular structures with natural language via an instruction-tuning approach, utilizing a two-stage training strategy that adeptly combines limited domain-specific data with molecular and textual information. InstructMol showcases substantial performance improvements in drug discovery-related molecular tasks, surpassing leading LLMs and significantly reducing the gap with specialized models, thereby establishing a robust foundation for a versatile and dependable drug discovery assistant.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data

    physics.chem-ph 2026-07 conditional novelty 6.0 of 10

    A two-stage AI pipeline — spectral hypothesis generation followed by mass-constrained molecular refinement — reconstructs organic structures from multimodal spectra, with 93.8% top-1 accuracy on simulated QM9 data and...

  2. ChemMLLM: Chemical Multimodal Large Language Model

    cs.LG 2025-05 reject novelty 6.0 of 10

    A chemical multimodal LLM is trained to understand and generate molecule images alongside SMILES and text, with claims of state-of-the-art results on five new tasks.

  3. CROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs

    q-bio.QM 2025-08 conditional novelty 5.0 of 10

    Cross-view prefix resampling, guided by the LLM's SMILES encoding, lets a Galactica-based model exploit molecular graphs and images at low context cost, improving captioning, IUPAC naming, and property prediction.

  4. A Comprehensive Data-centric Overview of Federated Graph Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A data-centric taxonomy for Federated Graph Learning that classifies 79 studies by data characteristics and data utilization, plus a discussion of integration with pre-trained large models.

  5. NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing

    cs.LG 2025-05 conditional novelty 5.0 of 10

    NOCL lets an LLM handle node, edge, and graph tasks on text and non-text graphs by compressing each node's description into one semantic embedding and turning the graph into a text prompt.

  6. Improving Chemical Understanding of LLMs via SMILES Parsing

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Pretraining LLMs on deterministic SMILES parsing tasks improves molecular structural understanding and downstream chemistry performance.

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