Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models

TL;DR

This paper introduces a novel framework using conditional residual energy-based models to improve molecule synthesis route generation, enabling control over route quality based on specific criteria and outperforming previous methods.

Contribution

The work presents a new probabilistic modeling approach that enhances synthetic route quality control and integrates seamlessly with existing strategies.

Findings

Boosts performance across various strategies

Outperforms state-of-the-art top-1 accuracy by 2.5%

Provides a plug-and-play framework for route quality improvement

Abstract

Molecule synthesis through machine learning is one of the fundamental problems in drug discovery. Current data-driven strategies employ one-step retrosynthesis models and search algorithms to predict synthetic routes in a top-bottom manner. Despite their effective performance, these strategies face limitations in the molecule synthetic route generation due to a greedy selection of the next molecule set without any lookahead. Furthermore, existing strategies cannot control the generation of synthetic routes based on possible criteria such as material costs, yields, and step count. In this work, we propose a general and principled framework via conditional residual energy-based models (EBMs), that focus on the quality of the entire synthetic route based on the specific criteria. By incorporating an additional energy-based function into our probabilistic model, our proposed algorithm can enhance the quality of the most probable synthetic routes (with higher probabilities) generated by various strategies in a plug-and-play fashion. Extensive experiments demonstrate that our framework can consistently boost performance across various strategies and outperforms previous state-of-the-art top-1 accuracy by a margin of 2.5%. Code is available at https://github.com/SongtaoLiu0823/CREBM.