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Table 1 Comparison of predictive performance in terms of RMSE

From: Improving chemical reaction yield prediction using pre-trained graph neural networks

Dataset

Split

Previous studies

Existing GNN pre-training methods

Proposed method

  

MFF [4]

YieldBERT [6]

YieldBERT-DA [7]

YieldMPNN [8]

From-Scratch

MolCLR [13]

DGI [18]

ContextPred [21]

AttrMasking [21]

MolDescPred-MPNN

MolDescPred

Buchwald-Hartwig (Random Split)

70/30

7.116±0.327

6.014±0.272

4.799±0.261

4.433±0.085

4.616±0.163

4.405±0.091

4.408±0.097

4.388±0.092

4.386±0.125

4.430±0.104

4.407±0.089

50/50

8.051±0.322

7.288±0.198

5.877±0.348

5.387±0.202

6.088±0.982

5.279±0.167

5.364±0.222

5.327±0.183

5.328±0.216

5.326±0.231

5.263±0.181

30/70

9.492±0.364

9.338±0.424

7.822±0.463

6.970±0.403

7.557±0.473

6.837±0.387

6.963±0.403

6.947±0.400

6.944±0.407

6.899±0.394

6.850±0.400

20/80

10.487±0.259

10.306±0.303

9.164±0.668

8.204±0.372

9.317±0.713

8.040±0.399

8.271±0.498

8.175±0.333

8.268±0.398

8.093±0.365

8.043±0.426

10/90

12.450±0.357

12.393±0.499

11.633±0.293

10.875±0.448

13.232±0.880

10.816±0.537

10.935±0.553

10.982±0.473

10.912±0.672

10.945±0.466

10.648±0.544

5/95

14.994±0.593

16.740±0.950

14.073±0.687

14.041±0.492

18.188±2.789

13.873±0.485

14.068±0.728

13.911±0.601

14.250±0.537

13.542±0.681

13.117±0.792

2.5/97.5

17.731±0.970

20.463±0.623

17.151±0.677

16.586±1.364

21.081±3.116

16.414±1.134

16.845±1.334

17.526±1.680

16.722±0.938

16.798±0.935

15.817±1.250

 

avg. rank

10.29±0.88

9.86±0.35

8.00±0.76

5.29±1.67

9.57±1.29

2.00±0.76

5.86±0.64

4.86±1.88

4.57±1.99

4.00±1.51

1.71±1.03

Suzuki-Miyaura (Random Split)

70/30

11.428±0.341

12.073±0.463

10.524±0.482

9.467±0.459

9.742±0.489

9.289±0.516

9.430±0.474

9.297±0.462

9.225±0.465

9.271±0.446

9.333±0.478

50/50

12.208±0.169

13.148±0.270

11.797±0.250

10.225±0.135

10.691±0.171

10.155±0.142

10.222±0.191

10.091±0.164

10.156±0.183

10.097±0.157

10.133±0.164

30/70

13.347±0.148

14.614±0.381

13.337±0.357

11.593±0.136

12.449±0.450

11.542±0.190

11.771±0.181

11.569±0.194

11.654±0.159

11.507±0.175

11.550±0.222

20/80

14.347±0.335

15.966±0.381

14.851±0.576

12.734±0.347

14.404±0.902

12.736±0.322

13.051±0.351

12.837±0.363

12.911±0.345

12.650±0.324

12.717±0.225

10/90

16.062±0.445

18.734±0.530

17.129±0.683

15.164±0.344

17.813±1.236

15.239±0.399

15.520±0.444

15.371±0.452

15.739±0.523

14.973±0.395

15.050±0.256

5/95

17.927±0.484

21.181±0.724

20.016±0.661

18.511±0.392

20.665±1.823

18.982±1.000

18.332±0.421

18.487±0.431

19.430±0.760

17.720±0.466

17.891±0.351

2.5/97.5

20.199±1.096

22.967±0.804

23.780±0.793

22.943±0.887

23.878±3.170

22.692±2.048

22.495±0.965

22.519±0.762

23.088±0.806

21.829±0.774

21.338±0.908

 

avg. rank

7.14±3.40

10.57±1.05

9.29±0.45

5.43±1.68

9.14±1.12

4.29±1.58

5.71±1.16

4.14±1.36

6.00±2.39

1.57±0.73

2.71±1.03

Buchwald-Hartwig (Out-Of-Sample Split)

Test 1

9.369±0.151

11.441±0.342

11.761±1.398

13.746±1.175

16.956±1.913

9.559±0.871

13.484±0.636

13.398±1.480

10.219±0.646

11.343±0.346

9.320±0.376

Test 2

14.163±0.155

11.144±1.267

9.886±0.741

9.476±1.027

9.474±0.829

9.274±1.016

11.511±1.711

9.439±1.103

8.883±0.697

9.860±1.349

8.002±0.472

Test 3

16.629±0.141

14.276±0.820

18.041±1.395

14.939±0.622

17.471±1.777

17.681±0.757

17.053±0.429

16.404±1.127

16.608±0.310

16.659±0.616

13.726±0.814

Test 4

20.698±0.135

19.679±1.397

24.279±0.494

18.774±0.566

19.954±3.058

19.044±0.370

23.295±0.244

22.858±1.064

19.229±0.587

19.507±0.745

20.780±0.767

 

avg.rank

6.50±3.20

5.50±2.50

9.25±1.79

5.00±3.39

7.75±2.38

4.50±3.20

9.25±0.83

6.25±2.28

3.50±1.12

5.75±1.30

2.75±3.03

  1. The best and second-best cases are highlighted in bold and underlined font, respectively