RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation (2311.14077v1)
Abstract: Retrosynthesis poses a fundamental challenge in biopharmaceuticals, aiming to aid chemists in finding appropriate reactant molecules and synthetic pathways given determined product molecules. With the reactant and product represented as 2D graphs, retrosynthesis constitutes a conditional graph-to-graph generative task. Inspired by the recent advancements in discrete diffusion models for graph generation, we introduce Retrosynthesis Diffusion (RetroDiff), a novel diffusion-based method designed to address this problem. However, integrating a diffusion-based graph-to-graph framework while retaining essential chemical reaction template information presents a notable challenge. Our key innovation is to develop a multi-stage diffusion process. In this method, we decompose the retrosynthesis procedure to first sample external groups from the dummy distribution given products and then generate the external bonds to connect the products and generated groups. Interestingly, such a generation process is exactly the reverse of the widely adapted semi-template retrosynthesis procedure, i.e. from reaction center identification to synthon completion, which significantly reduces the error accumulation. Experimental results on the benchmark have demonstrated the superiority of our method over all other semi-template methods.
- Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34:17981–17993, 2021.
- Learning to make generalizable and diverse predictions for retrosynthesis. arXiv preprint arXiv:1910.09688, 2019.
- Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au, 1(10):1612–1620, 2021.
- Computer-assisted retrosynthesis based on molecular similarity. ACS central science, 3(12):1237–1245, 2017.
- Elias James Corey. The logic of chemical synthesis. 1991.
- Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science, 166(3902):178–192, 1969.
- Diffdock: Diffusion steps, twists, and turns for molecular docking. In The Eleventh International Conference on Learning Representations, 2022.
- Sequence to sequence mixture model for diverse machine translation. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 583–592, 2018.
- Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
- Equivariant 3d-conditional diffusion models for molecular linker design. In NeurIPS 2022 AI for Science: Progress and Promises, 2022.
- Why are sequence-to-sequence models so dull? understanding the low-diversity problem of chatbots. In Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pp. 81–86, 2018.
- Valid, plausible, and diverse retrosynthesis using tied two-way transformers with latent variables. Journal of Chemical Information and Modeling, 61(1):123–133, 2021.
- Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pp. 8162–8171. PMLR, 2021.
- Decoding and diversity in machine translation. arXiv preprint arXiv:2011.13477, 2020.
- Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits. Journal of Chemical Information and Modeling, 61(7):3273–3284, 2021.
- What’s what: The (nearly) definitive guide to reaction role assignment. Journal of chemical information and modeling, 56(12):2336–2346, 2016.
- Modelling chemical reasoning to predict and invent reactions. Chemistry–A European Journal, 23(25):6118–6128, 2017.
- Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604–610, 2018.
- Gta: Graph truncated attention for retrosynthesis. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 531–539, 2021.
- A graph to graphs framework for retrosynthesis prediction. In International conference on machine learning, pp. 8818–8827. PMLR, 2020.
- Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pp. 2256–2265. PMLR, 2015.
- Learning graph models for template-free retrosynthesis. arXiv preprint arXiv:2006.07038, 2020.
- Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
- State-of-the-art augmented nlp transformer models for direct and single-step retrosynthesis. Nature communications, 11(1):5575, 2020.
- Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. Journal of chemical information and modeling, 62(15):3503–3513, 2022.
- Attention is all you need. Advances in neural information processing systems, 30, 2017.
- Digress: Discrete denoising diffusion for graph generation. In The Eleventh International Conference on Learning Representations, 2022.
- Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In International Conference on Machine Learning, pp. 22475–22490. PMLR, 2022.
- Retroprime: A diverse, plausible and transformer-based method for single-step retrosynthesis predictions. Chemical Engineering Journal, 420:129845, 2021.
- Retrosynthesis prediction with local template retrieval. arXiv preprint arXiv:2306.04123, 2023.
- Geodiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations, 2021.
- Retroxpert: Decompose retrosynthesis prediction like a chemist. Advances in Neural Information Processing Systems, 33:11248–11258, 2020.
- Predicting retrosynthetic reactions using self-corrected transformer neural networks. Journal of chemical information and modeling, 60(1):47–55, 2019.
- Root-aligned smiles: a tight representation for chemical reaction prediction. Chemical Science, 13(31):9023–9034, 2022.