用多模型融合提升合成路径可行性与多样性
A high-accuracy multi-model mixing retrosynthetic method
- 融合多个单步模型，提升预测准确性
- 保持反应总数不变，但显著提高可行路径比例
- 适合需高精度、多样化合成方案的化学家
近年来，计算机辅助合成规划（CASP）在各类算法基准上取得了显著进展。然而，实际应用中化学家常遇到大量不可行反应。本文分析了CASP常见错误，提出一种产物预测模型以提升单步模型的准确性。该模型虽减少单步反应数量，但通过集成多个单步模型，维持总体反应数量并增加反应多样性。基于人工分析与大规模测试，该产物预测模型结合多模型集成方法被证明具有更高可行性与更广反应多样性。
原文摘要 · Abstract (English)
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.
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