Retro-Fallback: Improving Retrosynthetic Planning by Accounting for Uncertainty in Chemical Reactions and Molecule Availability

Core Concepts

Retro-fallback is a novel algorithm that maximizes the probability that at least one synthesis plan can be executed in the lab by accounting for uncertainty in chemical reactions and molecule availability.

Abstract

The paper presents a novel formulation of retrosynthesis that accounts for uncertainty in the feasibility of chemical reactions and the availability of starting molecules. It introduces the concept of "successful synthesis probability" (SSP) as an evaluation metric that captures the probability that at least one synthesis plan can be executed.
The key insights are:

Uncertainty about reaction feasibility and molecule buyability can be represented using stochastic processes ξf and ξb.
SSP quantifies the probability that at least one synthesis plan in a set will be successful, given ξf and ξb.
The authors propose a greedy algorithm called retro-fallback that aims to maximize SSP by selecting frontier molecules for expansion based on their potential to improve SSP.
Retro-fallback outperforms existing algorithms like MCTS and retro* on in-silico benchmarks, demonstrating its effectiveness at finding sets of synthesis plans that are more likely to be executable in the lab.

The paper also discusses the computational complexity of estimating SSP, proving that it is generally intractable to compute exactly. The retro-fallback algorithm uses approximations to efficiently estimate SSP during the search.

Stats

The paper does not contain any key metrics or figures to support the author's main arguments. The results are presented qualitatively and through performance comparisons between algorithms.

Quotes

There are no direct quotes from the paper that support the key arguments.

Key Insights Distilled From

Retro-fallback

by Aust... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.09270.pdf

Deeper Inquiries

How could the retro-fallback algorithm be extended to also consider the quality (e.g. cost, number of steps) of the synthesis plans, in addition to their successful synthesis probability

To extend the retro-fallback algorithm to consider the quality of synthesis plans in addition to their successful synthesis probability, we can incorporate a cost function and a metric for the number of steps in the algorithm. Here's how we can achieve this:

Cost Function: Introduce a cost function that assigns a cost value to each reaction and starting molecule in a synthesis plan. The cost can be based on factors like the price of starting materials, the complexity of reactions, or the availability of reagents. The algorithm can then aim to minimize the total cost of the synthesis plan while maximizing the successful synthesis probability.

Number of Steps: Include a metric to measure the number of steps in a synthesis plan. This metric can capture the complexity and efficiency of the plan. The algorithm can prioritize synthesis plans with fewer steps, as shorter plans are generally more efficient and easier to execute in a laboratory setting.

Multi-Objective Optimization: Transform the problem into a multi-objective optimization task where the algorithm simultaneously considers successful synthesis probability, cost, and number of steps. This can be achieved by assigning weights to each objective and optimizing a weighted sum of these objectives.

By incorporating cost and number of steps into the optimization criteria, the retro-fallback algorithm can generate synthesis plans that not only have a high probability of success but are also cost-effective and efficient in terms of the number of steps required.

What are the limitations of the current stochastic process models for reaction feasibility and molecule buyability, and how could they be improved to better reflect real-world uncertainties

The current stochastic process models for reaction feasibility and molecule buyability have limitations that can be addressed to better reflect real-world uncertainties. Here are some ways to improve these models:

Incorporate More Data: Enhance the models by training them on larger and more diverse datasets of chemical reactions and molecule properties. More data can help capture a wider range of reactions and outcomes, leading to more accurate predictions.

Account for Reaction Conditions: Consider incorporating information about reaction conditions, such as temperature, pressure, and catalysts, into the models. These factors can significantly impact the feasibility of reactions and should be taken into account.

Dynamic Correlations: Develop models that can dynamically adjust correlations between reactions based on the context. For example, if certain reactions tend to occur together in specific conditions, the model should be able to learn and adapt these correlations.

Uncertainty Quantification: Implement methods to quantify uncertainty in the predictions of feasibility and buyability. This can provide chemists with a confidence level for the model's predictions and help them make informed decisions.

By addressing these limitations and incorporating these improvements, the stochastic process models can better capture the complexities and uncertainties of real-world chemical reactions and synthesis planning.

How could the retro-fallback algorithm be adapted to work with generative models that directly output synthesis plans, rather than searching over an explicit reaction graph

To adapt the retro-fallback algorithm to work with generative models that directly output synthesis plans, we can modify the algorithm's search strategy and decision-making process. Here's how we can make this adaptation:

Integration with Generative Models: Instead of relying on an explicit reaction graph, the algorithm can use the output of a generative model as the starting point for synthesis planning. The generative model can directly provide candidate synthesis plans, which the algorithm can then evaluate and refine.

Evaluation and Selection: The algorithm can evaluate the quality of the synthesis plans generated by the generative model based on criteria such as successful synthesis probability, cost, and number of steps. It can then select the most promising plans for further exploration or refinement.

Feedback Loop: Establish a feedback loop between the generative model and the algorithm, where the algorithm provides feedback on the quality of the generated synthesis plans. This feedback can be used to improve the generative model over time, leading to better-quality plans in subsequent iterations.

By integrating retro-fallback with generative models, we can leverage the strengths of both approaches to enhance the efficiency and effectiveness of retrosynthetic planning in the presence of uncertainty.