Skip to main content

Table 2 The statistical performance of SMPLIP-DNN models on PDBbind (Release 2015) according to different features compositions

From: SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

Features

Train

Valid

Test

LOSS

RMSE

PCC

LOSS

RMSE

PCC

LOSS

RMSE

PCC

IFP

0.563

0.871

0.899

1.019

1.483

0.678

1.032

1.538

0.726

IFP + Int-Dist

0.472

0.990

0.873

0.775

1.468

0.687

0.805

1.582

0.713

IFP + Frag

0.209

0.595

0.956

0.631

1.402

0.699

0.646

1.530

0.733

IFP + Int-Dist + Frag

0.212

0.403

0.979

0.834

1.400

0.733

0.923

1.559

0.714

  1. The Refined set (n = 3481) used for training and validation, and core set (n = 180) as a test data. The boldface represents the model with better statistics from different features combination