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Table 6 Comparison of our CNN and DeepTox (the winning model of the TOX 21 Challenge 2014)

From: Convolutional neural network based on SMILES representation of compounds for detecting chemical motif

Input

Model

Ave.

AR

AR-LBD

ER

ER-LBD

AhR

Aromatase

PPAR- γ

ARE

ATAD5

HSE

MMP

p53

SMILES Matrix

CNN

0.813

0.789

0.793

0.776

0.765

0.905

0.786

0.791

0.754

0.803

0.835

0.928

0.832

ECFP

DNN

0.768

0.850

0.690

0.840

0.760

0.660

0.720

0.700

0.730

0.860

0.810

0.820

0.780

ECFP+DeepTox

DNN

0.837

0.778

0.825

0.791

0.811

0.923

0.804

0.856

0.829

0.775

0.863

0.930

0.860

ECFP+DeepTox

SVM

0.832

0.882

0.748

0.799

0.798

0.919

0.819

0.856

0.818

0.781

0.848

0.946

0.854

ECFP+DeepTox

RF

0.820

0.776

0.812

0.770

0.746

0.917

0.806

0.827

0.810

0.786

0.826

0.945

0.835

ECFP+DeepTox

ElNet

0.803

0.788

0.692

0.765

0.805

0.897

0.763

0.805

0.778

0.768

0.844

0.924

0.818

  1. Our CNN takes SMILES feature matrices as input, while DeepTox uses ECFP and its original features