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Table 3 NDCG@10 of strategy I

From: When drug discovery meets web search: Learning to Rank for ligand-based virtual screening

 

AdaRank

RankNet

ListNet

PRank

RankBoost

SVMRank

SVR

ADORA3

0.4463

0.5885

0.5119

0.4032

0.6543

0.6446

0.6815

BDKRB2

0.5549

0.6186

0.4208

0.6242

0.5564

0.5917

0.5935

CB1

0.5913

0.4983

0.4586

0.6052

0.6993

0.7026

0.6921

CTSK

0.4225

0.3850

0.4741

0.4673

0.6545

0.5253

0.5199

CCK1

0.4122

0.5110

0.5704

0.7661

0.8523

0.7673

0.7136

CHRM1

0.1254

0.2978

0.1825

0.5366

0.6341

0.7076

0.7068

CHRM3

0.3295

0.5366

0.4880

0.7282

0.9019

0.8738

0.7277

TOP1

0.2076

0.3441

0.5005

0.6284

0.7746

0.8101

0.7387

EPHX2

0.4749

0.5997

0.5481

0.5506

0.6604

0.6102

0.5913

FBPase

0.5476

0.5420

0.5328

0.6281

0.8081

0.7810

0.7710

HMGCR

0.4078

0.5584

0.5475

0.6169

0.8089

0.7956

0.7660

Itgαvβ3

0.4168

0.3436

0.3555

0.4605

0.5837

0.5399

0.5360

JAK2

0.4208

0.3270

0.4184

0.5256

0.6804

0.6653

0.6548

KIF11

0.4682

0.4684

0.5724

0.5172

0.6912

0.7267

0.7163

LXR-beta

0.5828

0.5293

0.5009

0.6288

0.7260

0.7104

0.6899

mTOR

0.5204

0.5169

0.4038

0.6657

0.8334

0.8357

0.8517

MK2

0.5860

0.4398

0.4510

0.5909

0.7299

0.6945

0.7272

MMP-8

0.5792

0.4819

0.4843

0.5758

0.6699

0.6841

0.6815

ORL1

0.6082

0.3600

0.6024

0.6530

0.7270

0.7656

0.7430

PDE5

0.4877

0.6042

0.4628

0.5718

0.7368

0.7237

0.7117

EP3

0.4489

0.4484

0.5028

0.4054

0.6504

0.6292

0.6306

SGLT2

0.4619

0.3547

0.4285

0.5053

0.7047

0.6826

0.6843

CYP17

0.4829

0.4057

0.4001

0.4823

0.5637

0.4887

0.5231

ASC

0.4251

0.3584

0.4199

0.5630

0.6243

0.5629

0.5813

  1. The bold number among each row indicates the best performance among all the methods in this row.
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Journal of Cheminformatics

ISSN: 1758-2946

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