Table 1. Results of PCM using different combinations of ligand descriptors and four protein field descriptors (polar, lipophilic, unstable and stable water fields).

Ligand descriptors	Correlation (R2)	Predictability (Q2)	RMSEE a	RMSEPcv b	RMSEPtest c	R test 2 d
Random forest models
RDkit	0.957	0.737	0.360	0.799	0.810	0.716
MOE	0.961	0.703	0.360	0.857	0.840	0.695
4-PFP	0.928	0.566	0.480	1.025	0.990	0.569
GRIND	0.951	0.430	0.470	1.175	1.150	0.426
RDkit e	0.585	0.429	1.060	1.188	1.110	0.492
Target only models f	0.111	0.107	1.450	1.455	1.420	0.128
ID based models g	0.835	0.298	0.660	1.338	1.340	0.276
Partial least squares regression models with cross-terms
RDkit	0.671	0.588	0.884	1.024	1.007	0.557
MOE	0.504	0.433	1.085	1.194	1.129	0.439
4-PFP	0.554	0.451	1.029	1.216	1.136	0.437
GRIND	0.311	0.264	1.278	1.348	1.285	0.273
RDkit e	0.349	0.300	1.243	1.295	1.226	0.338
Target only models f	0.103	0.100	1.458	1.461	1.428	0.113
ID based models g	0.000	–0.001	45.282	45.307	43.439	0.000
RDkit (no cross-terms)	0.397	0.365	1.196	1.233	1.182	0.386
RDKit (only cross-terms) h	0.598	0.471	0.977	1.144	1.107	0.465

^aRoot-mean-square error of estimation for observations in the training set.

^bRoot-mean-square error of prediction resulting from 5-fold cross-validation.

^cRoot-mean-square error of prediction calculated using the external test set.

^dCorrelation between the observed and predicted values of the external test set.

^eGlobal QSAR models.

^fModels based on protein fields with exclusion of ligand descriptors.

^gModels with CHEMBL ids of compounds and targets used as descriptors.

^hModels based on cross-terms with exclusion of protein and ligand descriptors.