A machine learning approach for selecting substrate scope that balances performance and similarity

AI editorPublished 2026.09.14Posted 2026.09.15 12:01 KST

A preprint dated 2026-09-14 proposed ScopeBO, which considers performance together with substrate similarity and diversity when choosing a reaction substrate scope. Better performance than alternative methods was reported across several reaction datasets, but because it is still at the preprint stage, further validation and reproducibility in real synthetic contexts are needed. [1][2]

1/ AI 활용 방법: 머신러닝 알고리즘 ScopeBO and Vendi score-based diversity evaluation
2/ 적용 과제: reaction substrate scope selection and prioritization
3/ 보고된 결과: reported better performance than alternative selection methods on several reaction datasets
4/ A preprint released on 2026-09-14 proposed the machine learning-based method ScopeBO for reaction scope selection.
5/ This approach was designed to consider not only predictive performance but also similarity and diversity when choosing the substrate set to be tested, and it used the Vendi score as the diversity metric.
6/ It was reported to show better performance than alternative selection methods on several reaction datasets, and in materials and chemistry research it can be a reference for condition exploration or substrate library design.
7/ However, the currently confirmed results are based on a preprint, so peer-reviewed validation and extension to other chemical spaces must be confirmed separately. [1][2]

How to apply this

Rank substrate candidates by balancing performance and diversity to define an experimental scope more rationally.

Organize the substrate and performance information from past reaction data, and define the scope selection rules to be used as a baseline.
Use a method such as ScopeBO to score candidate substrate sets and reflect the balance between performance and diversity.
Test the top-ranked candidate set first in a small experimental set to see whether the predicted priorities match the actual results.
Add the results back in, rescore, and check whether the selection rule is overly biased toward any specific chemical class.

Data needed: Historical reaction data, structural or fingerprint features of each substrate, and information needed for performance metrics and similarity calculations are required.
Checking the result: Compare the selected scope with a random or simple rule-based scope under the same conditions to check whether performance and coverage actually improve.
What can go wrong: If you increase diversity only or pursue performance only, the result can be an unrepresentative scope, and experimental efficiency may fall even with a good score.

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A machine learning approach for selecting substrate scope that balances performance and similarity

How to apply this

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