Table 1 Comparison of yield predictions between the SEMG-MIGNN model with other SOTA models

From: Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Data splitting

Yield-BERT

DRFP

MFF

SEMG-MIGNN

Random 90/10

5.20 ± 0.500

5.09 ± 0.500

6.34 ± 0.500

4.79 ± 0.500

Random 70/30

5.82 ± 0.400

6.28 ± 0.300

6.77 ± 0.300

4.81 ± 0.400

Random 50/50

7.62 ± 0.500

7.36 ± 0.300

8.55 ± 0.300

6.83 ± 0.500

Random 30/70

9.41 ± 0.500

8.67 ± 0.500

10.09 ± 0.500

8.79 ± 0.700

Aryl Halidea

26.04 ± 0.300

26.19 ± 0.200

22.04 ± 0.200

19.34 ± 0.400

Additivea

21.29 ± 0.200

22.43 ± 0.200

21.66 ± 0.200

10.36 ± 0.200

Liganda

20.04 ± 0.200

18.35 ± 0.200

18.85 ± 0.200

11.02 ± 0.200

Basea

19.40 ± 0.200

19.90 ± 0.200

20.66 ± 0.200

14.52 ± 0.200

  1. Note: The best performance of each task is shown in bold. aThese data splitting tasks refer to the extrapolative predictions based on the scaffold splitting of the reaction components. Details are elaborated in Supplementary Fig. 20. RMSEs are in %.
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