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- W4303444705 abstract "Abstract Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data‐driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting‐edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles." @default.
- W4303444705 created "2022-10-07" @default.
- W4303444705 creator A5009790777 @default.
- W4303444705 creator A5022526821 @default.
- W4303444705 creator A5044100508 @default.
- W4303444705 creator A5049010282 @default.
- W4303444705 creator A5053550707 @default.
- W4303444705 creator A5070540983 @default.
- W4303444705 creator A5090667772 @default.
- W4303444705 date "2022-11-27" @default.
- W4303444705 modified "2023-10-17" @default.
- W4303444705 title "Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis" @default.
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