RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics

Molecular generation is a critical method in drug design, but its practical application is often limited by the difficulty of synthesizing the generated molecules. To address this challenge, we present RetroScore, a synthetic accessibility evaluation framework guided by multistep retrosynthesis. Our methodology integrates the semi-template model Graph2Edits with the multistep retrosynthesis planning algorithm Retro*, forming the Graph2Edits-Retro*d system. By incorporating the green chemistry metric of graph edit distance into the reaction cost function and a multistage screening protocol, this system identifies optimal routes while balancing reliability, synthetic efficiency, and economic feasibility. Benchmark evaluations demonstrate a 97.37% planning success rate with balanced optimization across route length, confidence score, and graph edit distance. In the molecular generation task, the RetroScore outperforms six of the seven synthetic accessibility metrics, yielding molecules with enhanced synthetic accessibility profiles across heterogeneous evaluation frameworks. To facilitate practical implementation, we developed an open-access web platform for automated retrosynthesis route prediction and RetroScore calculation, providing researchers with rapid synthetic accessibility assessments. The RetroScore web server is publicly accessible at http://aidd.bioai-global.com/RetroScore/ , and the source code is available at https://github.com/Snowgao320/RetroScore .

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Publication Details

Journal
Journal of Cheminformatics
Published
2025-12-19
DOI
https://doi.org/10.1186/s13321-025-01138-6
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics

Liang Lu, Jianping Lin, Song Wei Gao, Xiaofei Zhou
Journal of Cheminformatics
Computational Drug Discovery Methods
article

RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics

Liang Lu, Jianping Lin, Song Wei Gao, Xiaofei Zhou
article en

Abstract

Molecular generation is a critical method in drug design, but its practical application is often limited by the difficulty of synthesizing the generated molecules. To address this challenge, we present RetroScore, a synthetic accessibility evaluation framework guided by multistep retrosynthesis. Our methodology integrates the semi-template model Graph2Edits with the multistep retrosynthesis planning algorithm Retro*, forming the Graph2Edits-Retro*d system. By incorporating the green chemistry metric of graph edit distance into the reaction cost function and a multistage screening protocol, this system identifies optimal routes while balancing reliability, synthetic efficiency, and economic feasibility. Benchmark evaluations demonstrate a 97.37% planning success rate with balanced optimization across route length, confidence score, and graph edit distance. In the molecular generation task, the RetroScore outperforms six of the seven synthetic accessibility metrics, yielding molecules with enhanced synthetic accessibility profiles across heterogeneous evaluation frameworks. To facilitate practical implementation, we developed an open-access web platform for automated retrosynthesis route prediction and RetroScore calculation, providing researchers with rapid synthetic accessibility assessments. The RetroScore web server is publicly accessible at http://aidd.bioai-global.com/RetroScore/ , and the source code is available at https://github.com/Snowgao320/RetroScore .

Journal of CheminformaticsVol. 18(1)
Tianjin University of Science and Technology (CN), Nankai University (CN), Bio-Medical Science (South Korea) (KR), State Key Laboratory of Medicinal Chemical Biology
Chinese Academy of Sciences
Openalex Percentile: Top 60%
Computational Drug Discovery Methods
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RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics — Liang Lu, Jianping Lin, et al. · Journal of Cheminformatics (2025) | TGRS Research Map | TGRS