A high-accuracy multi-model mixing retrosynthetic method

TL;DR

This paper introduces a multi-model ensemble approach combined with a product prediction model to improve the feasibility and diversity of computer-aided synthesis planning reactions.

Contribution

It presents a novel multi-model mixing retrosynthetic method that enhances reaction feasibility and diversity in CASP by integrating multiple models.

Findings

Higher reaction feasibility achieved

Increased reaction diversity demonstrated

Reduced infeasible reactions in practice

Abstract

The field of computer-aided synthesis planning (CASP) has seen rapid advancements in recent years, achieving significant progress across various algorithmic benchmarks. However, chemists often encounter numerous infeasible reactions when using CASP in practice. This article delves into common errors associated with CASP and introduces a product prediction model aimed at enhancing the accuracy of single-step models. While the product prediction model reduces the number of single-step reactions, it integrates multiple single-step models to maintain the overall reaction count and increase reaction diversity. Based on manual analysis and large-scale testing, the product prediction model, combined with the multi-model ensemble approach, has been proven to offer higher feasibility and greater diversity.