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DiffIUPAC

Diffusion-based generative drug-like molecular editing with chemical natural language

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Model Architecture of DiffIUPAC

Acknowledgements

We thank the authors of C5T5: Controllable Generation of Organic Molecules with Transformers, IUPAC2Struct: Transformer-based artificial neural networks for the conversion between chemical notations, Deep molecular generative model based on variant transformer for antiviral drug design, and SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers for releasing their code. The code in this repository is based on their source code release (https://github.com/dhroth/c5t5, https://github.com/sergsb/IUPAC2Struct, https://github.com/AspirinCode/TransAntivirus, and https://github.com/yuanhy1997/seqdiffuseq). If you find this code useful, please consider citing their work.

News!

[2024/11/02] Available online Journal of Pharmaceutical Analysis, 2024.

[2024/10/29] Accepted in Journal of Pharmaceutical Analysis, 2024.

[2024/05/14] submission to Journal of Pharmaceutical Analysis, 2024.

Requirements

conda create -n diffiupac python=3.8
conda install mpi4py
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0
pip install -r requirements.txt

https://github.com/rdkit/rdkit

System Requirerments

requires system memory larger than 228GB.
(if GPU is available) requires GPU memory larger than 80GB.

Data

PubChem

https://pubchem.ncbi.nlm.nih.gov/

IUPAC Name-Canonical SMILES pairs

#example：Aspirin
2-acetyloxybenzoic acid | CC(=O)OC1=CC=CC=C1C(=O)O

IUPAC name ⇆ SMILES string

Structure/SMILES2IUPAC

IUPAC Naming

https://web.chemdoodle.com/demos/iupac-naming

SMILES2IUPAC

https://huggingface.co/knowledgator/SMILES2IUPAC-canonical-base

Smiles-TO-iUpac-Translator

https://github.com/Kohulan/Smiles-TO-iUpac-Translator

IUPAC2SMILES

https://www.antvaset.com/iupac-to-smiles

https://web.chemdoodle.com/demos/iupac-naming

Training

To run the code, we use iwslt14 en-de as an illustrative example:

Prepare the data: Learning the BPE tokenizer by

sh ./tokenizer_utils.py train-byte-level iwslt14 10000

To train with the following line:

mkdir ckpts
bash ./train_scripts/train.sh 0 iupac smiles
#(for en to de translation) bash ./train_scripts/iwslt_en_de.sh 0 smiles iupac

You may modify the scripts in ./train_scripts for your own training settings.

To fine tune with the following line:

bash ./train_scripts/fine_tune.sh 0 iupac smiles

Generating

To run the code, example data is in the example folder:

bash ./train_scripts/gen_opt.sh

Model Metrics

MOSES

Molecular Sets (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, MOSES aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models.
https://github.com/molecularsets/moses

QEPPI

quantitative estimate of protein-protein interaction targeting drug-likeness

https://github.com/ohuelab/QEPPI

License

Code is released under GNU GENERAL PUBLIC LICENSE.

Cite:

J. Wang, P. Zhou, Z. Wang, W. Long, Y. Chen, K.T. No, D. Ouyang, J. Mao, X. Zeng, Diffusion-based generative drug-like molecular editing with chemical natural language, Journal of Pharmaceutical Analysis, https://doi.org/10.1016/j.jpha.2024.101137.
Jiashun Mao, Jianmin Wang, Amir Zeb, Kwang-Hwi Cho, Haiyan Jin, Jongwan Kim, Onju Lee, Yunyun Wang, and Kyoung Tai No. "Transformer-Based Molecular Generative Model for Antiviral Drug Design" Journal of Chemical Information and Modeling, 2023;, DOI: 10.1021/acs.jcim.3c00536
Yuan, Hongyi, Zheng Yuan, Chuanqi Tan, Fei Huang, and Songfang Huang. "SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers." arXiv preprint arXiv:2212.10325 (2022).
Rothchild, Daniel, Alex Tamkin, Julie Yu, Ujval Misra, and Joseph Gonzalez. "C5t5: Controllable generation of organic molecules with transformers." arXiv preprint arXiv:2108.10307 (2021).