A high-accuracy multi-model mixing retrosynthetic method
This addresses the issue of practical usability for chemists by improving CASP accuracy, though it appears incremental as it builds on existing single-step models.
The paper tackled the problem of infeasible reactions in computer-aided synthesis planning (CASP) by introducing a product prediction model that reduces single-step reactions while integrating multiple models to maintain reaction count and diversity, resulting in higher feasibility and greater diversity based on manual analysis and large-scale testing.
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.