3D-MoLM: Towards 3D Molecule-Text Interpretation in Language Models

Codes of our ICLR'24 paper. [Paper Link], [Website], [Demo]

Authors: Sihang Li*, Zhiyuan Liu*, Yanchen Luo, Xiang Wang†, Xiangnan He†, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian

* Equal Contribution

† Corresponding

Framework

3D-MoLM is a versatile molecular LM that can be applied for molecule-text retrieval, molecule captioning, and molecule question-answering tasks.
3D-MoLM employs a 3D molecule-text projector to bridge the modality gap between a 3D molecular encoder and an LM, enabling the LM to perceive 3D molecular structures.

Training pipeline

To tackle the two challenges of 3D molecule-text alignment and 3D molecule-centric instruction tuning, we delineate a three-stage training pipeline for 3D-MoLM, including 1) 3D molecule-text representation learning, 2) 3D molecule-text alignment via text generation, and 3) instruction-based fine-tuning.

3D Molecule-Text Alignment

3D Molecule-Text Alignment maps 3D molecular representations into the input textual space where the LM can understand.

Data Preparation – PubChem. We collect molecular SMILES-text pairs from PubChem, and further employ GPT-3.5 to enrich the less annotated molecular descriptions. Molecular 3D conformations are obtained by running the MMFF algorithm in RDKit. As a result, 316K 3D molecule-text pairs are obtained for the alignment pretraining and downstream tasks of molecule-text retrieval and molecule captioning.
Stage 1: 3D Molecule-Text Representation Learning cultivates Q-Former’s ability to extract molecular features that resonate profoundly with the corresponding text
Stage 2: 3D Molecule-Text Alignment via Generative Learning connect the Q-Former with the LM to leverage the LM’s language generation capability, where the molecular representations extracted by the Q-Former serve as 1D soft prompts comprehensible to the LM.

3D Molecule-centric Instruction Tuning

3D Molecule-centric Instruction Tuning fine-tunes the model to follow human instructions on 3D molecule relevant tasks.

Data Preparation – 3D-MoIT. We construct 3D-MoIT by sourcing data from the PubChem and PubChemQC databases, including both computed molecular properties and descriptive texts that characterize molecule properties in numerical values and free texts, respectively.
Stage 3: Instruction-based Fine-tuning enhances the ability to follow various instructions and improves the understanding of 3D molecular structures, especially in recognizing 3D-dependent properties

Demonstration

Molecule Captioning

Open-text QA

Computed Property QA

Requirements

Key dependencies include:

torch==2.0.1
transformers==4.35.0
deepspeed==0.12.2
pytorch-lightning==2.0.7
uni-core==0.0.1

See requirements.txt for more detailed requirements.

Dataset

3D-MoIT. Download the dataset from Huggingface-3D-MoIT, and put it under the ./data/ directory.

Checkpoints

Download following checkpoints from Huggingface-3D-MoLM, and put it under the ./all_checkpoints/ directory.

3D-MoLM
Uni-Mol
SciBERT
Llama2-7B

Inference

Run inference.ipynb to play with 3D-MoLM.

Reproduce the results

Prerequisites

Install required conda environment as described in Requirements section
Download the dataset and required checkpoints as described in Dataset and Checkpoints section.

Directly Read the Outputs

We provide the outputs of 3D-MoLM on the test set of 3D-MoIT in all_checkpoints/generalist/lightning_logs/version_0/predictions.txt. Run the following script to read it.

python read_generalist_results.py 
    --file_path 'all_checkpoints/generalist/lightning_logs/version_0/predictions.txt' 
    --tokenizer_path 'all_checkpoints/llama-2-7b-hf'

Evaluation on Our Pretrained Checkpoint

We share the checkpoint for reproducing results.

bash ./scripts/stage3_test.sh

Training the Model from Scratch

Stage 1: 3D Molecule-Text Representation Learning

Run the following script for stage 1 pretraining:

bash ./scripts/stage1_pretrain.sh

Stage 2: 3D Molecule-Text Alignment via Generative Learning

Run the following script for stage 2 pretraining:

bash ./scripts/stage2_pretrain.sh

Stage 3: Instruction-based Fine-tuning

Run the following script for instruction tuning:

bash ./scripts/stage3_train.sh

Citation

If you use our codes or checkpoints, please cite our paper:

@inproceedings{li2024molm,
    title={3D-MoLM: Towards 3D Molecule-Text Interpretation in Language Models},
    author={Li, Sihang and Liu, Zhiyuan and Luo, Yanchen and Wang, Xiang and He, Xiangnan and Kawaguchi, Kenji  and Chua, Tat-Seng and Tian, Qi},
    booktitle={ICLR},
    year={2024},
    url={https://openreview.net/forum?id=xI4yNlkaqh}
}

cxxly/3D-MoLM