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Table 5 Top potential CYP2B6 and CYP2C8 inhibitor drugs based on the highest composite score

From: Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning

CYP2B6 potential inhibitors

No.

DrugBank ID

CHEMBL ID

Drug's name

Probability score

Maximum Tanimoto’s similarity

Composite score (0.7 prob + 0.3 max. Tanimoto’s sim.)

1

DB06290

CHEMBL501849

Simeprevir

0.979

0.928

0.964

2

DB11633

CHEMBL409153

Isavuconazole

1.000

0.868

0.960

3

DB00377

CHEMBL1189679

Palonosetron

0.997

0.792

0.935

4

DB11340

–

Ubiquinol

1.000

0.780

0.934

5

DB00758

CHEMBL1771

Clopidogrel

0.984

0.771

0.920

6

DB11254

CHEMBL443605

Hexylresorcinol

0.999

0.730

0.919

7

DB14120

CHEMBL3961037

Phenylethyl resorcinol

0.999

0.727

0.918

8

DB00735

CHEMBL626

Naftifine

0.992

0.724

0.912

9

DB05239

CHEMBL2146883

Cobimetinib

1.000

0.700

0.910

10

DB12612

CHEMBL3707247

Ozanimod

1.000

0.694

0.964

CYP2C8 potential inhibitors

No.

DrugBank ID

CHEMBL ID

Drug's name

Probability score

Maximum Tanimoto’s similarity

Composite score (0.7 prob + 0.3 max. Tanimoto’s sim.)

1

DB00528

CHEMBL250270

Lercanidipine

0.998

0.912

0.972

2

DB09238

CHEMBL1085699

Manidipine

0.988

0.910

0.965

3

DB14086

CHEMBL311498

Cianidanol

0.971

0.875

0.942

4

DB13946

CHEMBL2107067

Testosterone undecanoate

1.000

0.783

0.935

5

DB11340

–

Ubiquinol

1.000

0.780

0.934

6

DB13944

CHEMBL1200335

Testosterone enanthate

0.999

0.783

0.934

7

DB14989

CHEMBL3948730

Umbralisib

1.000

0.746

0.924

8

DB13943

CHEMBL1201101

Testosterone cypionate

1.000

0.739

0.922

9

DB14914

CHEMBL3545253

Flortaucipir F-18

0.999

0.727

0.918

10

DB12364

CHEMBL512351

Betrixaban

0.999

0.714

0.972