MoleculeSTM

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
Nature Machine Intelligence 2023

¹Mila
²Université de Montréal
³Nvidia Research
⁴University of Illinois Urbana-Champaign
⁵California Institute of Technology

⁶Princeton University
⁷HEC Montréal
⁸CIFAR AI Chair
⁹Arizona State University

^♠Work done during intern at Nvidia Research

^♦Equal advising

MoleculeSTM pipeline

Downstream: Zero-shot Structure-text Retrieval

Downstream: Zero-shot Text-based Molecule Editing

Downstream: Molecular Property Prediction

Citation

@article{liu2023moleculestm,
    title={Multi-modal molecule structure-text model for text-based retrieval and editing},
    author={Liu, Shengchao and Nie, Weili and Wang, Chengpeng and Lu, Jiarui and Qiao, Zhuoran and Liu, Ling and Tang, Jian and Xiao, Chaowei and Anandkumar, Anima},
    journal={Nature Machine Intelligence},
    year={2023},
    month={Dec},
    day={01},
    volume={5},
    number={12},
    pages={1447-1457},
    issn={2522-5839},
    doi={10.1038/s42256-023-00759-6},
    url={https://doi.org/10.1038/s42256-023-00759-6}
}

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
Nature Machine Intelligence 2023

Paper

Code

MoleculeSTM pipeline

Downstream: Zero-shot Structure-text Retrieval

Downstream: Zero-shot Text-based Molecule Editing

Downstream: Molecular Property Prediction

Citation

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval Nature Machine Intelligence 2023

Paper

Code

MoleculeSTM pipeline

Downstream: Zero-shot Structure-text Retrieval

Downstream: Zero-shot Text-based Molecule Editing

Downstream: Molecular Property Prediction

Citation

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
Nature Machine Intelligence 2023