Mind the Retrosynthesis Gap: Bridging the divide between Single-step and
Multi-step Retrosynthesis Prediction
- URL: http://arxiv.org/abs/2212.11809v1
- Date: Mon, 12 Dec 2022 18:06:24 GMT
- Title: Mind the Retrosynthesis Gap: Bridging the divide between Single-step and
Multi-step Retrosynthesis Prediction
- Authors: Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden, Jonas
Verhoeven, Mike Preuss, Igor Tetko
- Abstract summary: Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models.
We show that models designed for single-step retrosynthesis, when extended to multi-step, can have a tremendous impact on the route finding capabilities of current multi-step methods.
- Score: 0.9134244356393664
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Retrosynthesis is the task of breaking down a chemical compound recursively
step-by-step into molecular precursors until a set of commercially available
molecules is found. Consequently, the goal is to provide a valid synthesis
route for a molecule. As more single-step models develop, we see increasing
accuracy in the prediction of molecular disconnections, potentially improving
the creation of synthetic paths. Multi-step approaches repeatedly apply the
chemical information stored in single-step retrosynthesis models. However, this
connection is not reflected in contemporary research, fixing either the
single-step model or the multi-step algorithm in the process. In this work, we
establish a bridge between both tasks by benchmarking the performance and
transfer of different single-step retrosynthesis models to the multi-step
domain by leveraging two common search algorithms, Monte Carlo Tree Search and
Retro*. We show that models designed for single-step retrosynthesis, when
extended to multi-step, can have a tremendous impact on the route finding
capabilities of current multi-step methods, improving performance by up to +30%
compared to the most widely used model. Furthermore, we observe no clear link
between contemporary single-step and multi-step evaluation metrics, showing
that single-step models need to be developed and tested for the multi-step
domain and not as an isolated task to find synthesis routes for molecules of
interest.
Related papers
- One Step Diffusion via Shortcut Models [109.72495454280627]
We introduce shortcut models, a family of generative models that use a single network and training phase to produce high-quality samples.
Shortcut models condition the network on the current noise level and also on the desired step size, allowing the model to skip ahead in the generation process.
Compared to distillation, shortcut models reduce complexity to a single network and training phase and additionally allow varying step budgets at inference time.
arXiv Detail & Related papers (2024-10-16T13:34:40Z) - Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model [5.044138778500218]
Retrosynthesis is a fundamental but challenging task in organic chemistry.
Given a target molecule, the goal of retrosynthesis is to find out a series of reactions which could be assembled into a synthetic route.
We propose a new retrosynthetic planning algorithm called retro-prob to maximize the successful synthesis probability of target molecules.
arXiv Detail & Related papers (2024-05-25T08:23:40Z) - DirectMultiStep: Direct Route Generation for Multi-Step Retrosynthesis [0.0]
We introduce a transformer-based model that generates multi-step synthetic routes as a single string by conditionally predicting each molecule based on all preceding ones.
The model accommodates specific conditions such as the desired number of steps and starting materials, outperforming state-of-the-art methods on the PaRoutes dataset.
It also successfully predicts routes for FDA-approved drugs not included in the training data, showcasing its generalization capabilities.
arXiv Detail & Related papers (2024-05-22T20:39:05Z) - UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment [51.49238426241974]
This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction.
By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules.
arXiv Detail & Related papers (2024-03-25T03:23:03Z) - One-Step Diffusion Distillation via Deep Equilibrium Models [64.11782639697883]
We introduce a simple yet effective means of distilling diffusion models directly from initial noise to the resulting image.
Our method enables fully offline training with just noise/image pairs from the diffusion model.
We demonstrate that the DEQ architecture is crucial to this capability, as GET matches a $5times$ larger ViT in terms of FID scores.
arXiv Detail & Related papers (2023-12-12T07:28:40Z) - Evolutionary Retrosynthetic Route Planning [8.19851864095418]
This paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization.
It marks the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis.
arXiv Detail & Related papers (2023-10-08T14:47:41Z) - RetroBridge: Modeling Retrosynthesis with Markov Bridges [2.256703675017117]
Retrosynthesis planning aims at designing reaction pathways from commercially available starting materials to a target molecule.
We introduce the Markov Bridge Model, a generative framework aimed to approximate the dependency between two discrete distributions.
We then address the retrosynthesis planning problem with our novel framework and introduce RetroBridge, a template-free retrosynthesis modeling approach.
arXiv Detail & Related papers (2023-08-30T15:09:22Z) - Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis
Planning [0.8620335948752805]
Retrosynthesis consists of breaking down a chemical compound step-by-step into molecular precursors.
Its two primary research directions, single-step retrosynthesis prediction and multi-step synthesis planning, are inherently intertwined.
We show that the choice of the single-step model can improve the overall success rate of synthesis planning by up to +28%.
arXiv Detail & Related papers (2023-08-10T12:04:47Z) - Retrosynthetic Planning with Dual Value Networks [107.97218669277913]
We propose a novel online training algorithm, called Planning with Dual Value Networks (PDVN)
PDVN alternates between the planning phase and updating phase to predict the synthesizability and cost of molecules.
On the widely-used USPTO dataset, our PDVN algorithm improves the search success rate of existing multi-step planners.
arXiv Detail & Related papers (2023-01-31T16:43:53Z) - Retrieval-based Controllable Molecule Generation [63.44583084888342]
We propose a new retrieval-based framework for controllable molecule generation.
We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria.
Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
arXiv Detail & Related papers (2022-08-23T17:01:16Z) - RetroXpert: Decompose Retrosynthesis Prediction like a Chemist [60.463900712314754]
We devise a novel template-free algorithm for automatic retrosynthetic expansion.
Our method disassembles retrosynthesis into two steps.
While outperforming the state-of-the-art baselines, our model also provides chemically reasonable interpretation.
arXiv Detail & Related papers (2020-11-04T04:35:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.