MoleculeSTM

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval

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

Text-based Molecule Editing for Binding.

Binding task from ChEMBL (ChEMBL 1613777). The text prompt is "This molecule is tested positive in an assay that are inhibitors and substrates of an enzyme protein. It uses molecular oxygen inserting one oxygen atom into a substrate, and reducing the second into a water molecule."
The visualizations are for the two output molecules edited with MoleculeSTM.
For the detailed structure of the input molecules, please check Figure 6.

MoleculeSTM pipeline

Downstream: Zero-shot Structure-text Retrieval

Downstream: Zero-shot Text-based Molecule Editing

Downstream: Molecular Property Prediction

Citation

@article{liu2022moleculeSTM,
    title={Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval},
    author={Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar},
    journal={arXiv preprint arXiv:2212.10789},
    year={2022}
}

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval

Paper

Code

MoleculeSTM pipeline

Downstream: Zero-shot Structure-text Retrieval

Downstream: Zero-shot Text-based Molecule Editing

Downstream: Molecular Property Prediction

Citation