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Table 2 Comparison of predictive performance in terms of MAE

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

4.694±0.116

3.990±0.153

3.090±0.118

2.920±0.056

3.038±0.096

2.896±0.060

2.909±0.060

2.888±0.060

2.905±0.049

2.921±0.054

2.899±0.061

50/50

5.370±0.134

4.792±0.124

3.744±0.150

3.497±0.090

3.957±0.796

3.420±0.054

3.488±0.074

3.465±0.057

3.485±0.078

3.463±0.082

3.439±0.054

30/70

6.471±0.183

6.075±0.222

4.833±0.167

4.483±0.165

4.873±0.244

4.400±0.152

4.489±0.150

4.462±0.132

4.496±0.160

4.439±0.137

4.408±0.147

20/80

7.271±0.200

6.862±0.212

5.781±0.252

5.311±0.154

6.119±0.415

5.197±0.169

5.345±0.203

5.309±0.146

5.392±0.170

5.240±0.170

5.196±0.187

10/90

8.962±0.308

8.607±0.387

7.705±0.236

7.196±0.274

9.077±0.809

7.158±0.269

7.304±0.268

7.286±0.209

7.269±0.359

7.266±0.250

7.061±0.262

5/95

11.085±0.322

12.117±0.789

9.651±0.338

9.677±0.408

14.043±2.879

9.932±0.408

9.688±0.467

9.614±0.393

9.716±0.392

9.434±0.418

9.058±0.463

2.5/97.5

13.592±0.950

15.979±0.817

12.243±0.631

11.747±1.005

16.003±2.434

11.903±0.815

11.870±0.823

12.512±1.239

11.775±0.647

12.075±0.622

11.304±0.952

 

avg. rank

10.29±0.88

9.86±0.35

7.43±1.50

4.71±1.58

9.71±1.16

3.00±2.39

5.71±0.88

4.29±2.05

5.43±1.50

4.00±1.69

1.57±0.73

Suzuki-Miyaura (Random Split)

70/30

7.904±0.169

8.128±0.344

6.598±0.270

6.116±0.223

6.323±0.245

6.038±0.264

6.096±0.263

6.053±0.253

6.037±0.243

6.038±0.226

6.045±0.218

50/50

8.522±0.118

8.922±0.235

7.539±0.153

6.725±0.089

7.053±0.133

6.676±0.088

6.729±0.138

6.661±0.119

6.702±0.141

6.629±0.112

6.667±0.101

30/70

9.502±0.106

10.094±0.346

8.804±0.249

7.847±0.094

8.502±0.295

7.778±0.134

7.953±0.109

7.822±0.120

7.887±0.116

7.751±0.082

7.793±0.147

20/80

10.360±0.212

11.229±0.247

10.017±0.338

8.793±0.191

10.008±0.613

8.785±0.181

9.022±0.194

8.890±0.227

8.918±0.207

8.691±0.213

8.775±0.161

10/90

11.890±0.268

13.528±0.395

11.954±0.443

10.739±0.211

12.839±1.154

10.863±0.249

11.017±0.304

10.948±0.320

11.171±0.330

10.591±0.233

10.781±0.182

5/95

13.545±0.281

15.695±0.618

14.294±0.507

13.451±0.353

15.307±1.530

14.691±1.191

13.381±0.301

13.543±0.248

14.120±0.513

12.934±0.364

13.236±0.299

2.5/97.5

15.640±0.813

17.666±0.496

17.587±0.690

17.189±0.813

18.289±2.538

18.129±2.291

16.928±0.737

16.817±0.467

16.997±0.716

16.324±0.593

16.114±0.697

 

avg. rank

7.86±3.14

10.71±0.70

8.71±0.45

5.00±1.69

9.00±1.20

4.86±3.04

5.86±1.36

4.29±1.03

5.43±1.92

1.43±0.73

2.71±0.70

Buchwald-Hartwig (Out-Of-Sample Split)

Test 1

6.682±0.101

7.351±0.099

7.015±0.758

8.082±0.827

10.941±1.385

6.358±0.605

7.955±0.344

8.357±1.108

6.609±0.411

7.020±0.173

5.980±0.231

Test 2

9.459±0.112

7.266±0.724

6.588±0.328

6.300±0.647

6.359±0.524

6.412±0.637

7.649±0.893

6.421±0.607

5.997±0.499

6.398±0.785

5.469±0.396

Test 3

10.282±0.150

9.129±0.745

11.052±0.950

8.986±0.314

11.021±1.509

11.154±0.596

10.240±0.546

9.780±1.087

10.106±0.268

10.639±0.576

8.340±0.351

Test 4

14.874±0.050

13.671±1.067

18.422±0.620

13.190±0.754

14.414±2.982

13.231±0.266

16.719±0.598

16.084±1.174

13.910±0.320

13.616±0.597

13.870±0.393

 

avg.rank

7.50±2.50

5.75±2.38

8.50±2.29

3.75±3.11

7.75±2.59

5.25±3.70

8.50±1.66

7.50±2.29

4.00±1.58

5.50±1.80

2.00±1.73

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