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Translation between Molecules and Natural Language

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arxiv 2204.11817 v3 pith:MZTCQ2KR submitted 2022-04-25 cs.CL cs.AI

Carl Edwards , Tuan Lai , Kevin Ros , Garrett Honke , Kyunghyun Cho , Heng Ji This is my paper · ORCID
classification cs.CLcs.AI
keywords moleculemolt5textbflanguagemodelsmoleculescaptioningdata
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time. Since $\textbf{MolT5}$ pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity. Furthermore, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation. Our results show that $\textbf{MolT5}$-based models are able to generate outputs, both molecules and captions, which in many cases are high quality.

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Forward citations

Cited by 10 Pith papers

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

  1. Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Training LLMs first on bidirectional SMILES–graph conversion plus progressive CoT yields large structure-perception gains and better property prediction and molecular optimization.

  2. MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation

    q-bio.BM 2025-07 conditional novelty 6.0 of 10

    A single masked-diffusion model trained jointly on four molecular-editing tasks outperforms or matches task-specific diffusion baselines across docking, chemical property, and geometry metrics.

  3. DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning

    q-bio.QM 2025-07 conditional novelty 6.0 of 10

    DeepRetro combines LLM-generated retrosynthetic disconnections with template-based search and human feedback, achieving strong benchmark results and proposing new routes for complex natural products.

  4. ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A modular LLM-centric framework for molecular relational learning that supports 1D, 2D, and 3D molecular inputs and flexible model assembly, benchmarked across DDI, SSI, and CSI tasks.

  5. 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.

  6. NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra

    physics.chem-ph 2025-12 conditional novelty 5.0 of 10

    NMIRacle generates molecular structures from combined raw IR and NMR spectra, improving Top-1 elucidation accuracy from 0.41 to 0.48 over the NMR2Struct baseline on the Alberts benchmark.

  7. An Encoder-Decoder Foundation Chemical Language Model for Generative Polymer Design

    cond-mat.mtrl-sci 2025-10 conditional novelty 5.0 of 10

    A T5-based polymer language model pre-trained on 100 million hypothetical polymers predicts thermal, electronic, and solubility properties and generates polymers conditioned on a target glass-transition temperature, w...

  8. HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling

    cs.LG 2025-08 reject novelty 5.0 of 10

    HSA-Net improves molecular language modeling by adaptively switching between cross-attention and Mamba projectors across GNN layers and fusing the results with a sparse mixture-of-experts.

  9. 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.

  10. Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

    cs.LG 2026-07 reject novelty 4.0 of 10

    LLMol fine-tunes an LLM on simplified SELFIES and uses GRPO with RDKit-derived rewards for targeted molecular generation, but its own benchmark tables contradict the claimed state-of-the-art performance.

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