Convergent QSAR studies on a series of NK₃ receptor antagonists for schizophrenia treatment

Figures & data

Table 1. 2D structure of data set compounds with its K_i and pK_i values.

Training set
N-benzyl amide analogues
Cpd	Stereochemistry	Ar	Ki (nM)	pKi
1	(1R,2S)	C6H5	83	7.081
2	(1S,2R)	4-F-C6H4	5.8	8.237
Aromatic ring on cyclopropane core
Compound	Stereochemistry	Ar	Ki (nM)	pKi
3	(1S,2R)	4-F-C6H4	67	7.174
4	(1S,2R)	4-Cl-C6H4	36	7.444
5	(1S,2R)	4-CF3-C6H4	1000	6.000
6	(1S,2R)	4-CN-C6H4	270	6.569
7	(1S,2R)	3-F-C6H4	140	6.854
8	(1S,2R)	3-Cl-C6H4	32	7.495
9	(1S,2R)	3-Me-C6H4	40	7.398
10	(1S,2R)	3-OMe-C6H4	56	7.252
11	(1S,2R)	3,4-F2-C6H3	47	7.328
12	(1S,2R)	3,4-Cl2-C6H3	5.8	8.237
13	(1S,2R)	3,4-Me-C6H3	7.3	8.137
14	(1S,2R)	3,4-OCH2O-C6H3	140	6.854
15	(1S,2R)	2-tiofenil	93	7.032
16	(1S,2R)	3-tiofenil	110	6.959
Phenyl piperidine analogues
Cpd	Stereochemistry	R	Ki (nM)	pKi
17	(1S,2R)		4.2	8.377
18	(1S,2R)		6.1	8.215
19	(1S,2R)		1.5	8.824
20	(1S,2R)		5.6	8.252
21	(1S,2R)		12.0	7.921
22	(1S,2R)		3.6	8.444
23	(1S,2R)		7.8	8.108
Piperazine analogues
Cpd	Stereochemistry	NR^1R^2	Ki (nM)	pKi
24	(1S,2R)		3.3	8.481
25	(1S,2R)		3.7	8.432
26	(1S,2R)		1.8	8.745
27	(1S,2R)		17	7.770
28	(1S,2R)		7.3	8.137
29	(1S,2R)		7.6	8.119
30	(1S,2R)		5.8	8.237
31	(1S,2R)		1.9	8.721
32	(1S,2R)		150	6.824
33	(1S,2R)		140	6.854
Test set
N-benzyl amide analogues
Cpd	Stereochemistry	Ar	Ki (nM)	pKi
34	(1S,2R)	C6H5	5.9	8.229
Aromatic ring on cyclopropane core
Compound	Stereochemistry	Ar	Ki (nM)	pKi
35	(1S,2R)	4-Me-C6H4	37	7.432
36	(1S,2R)	4-OMe-C6H4	350	6.456
37	(1S,2R)	3-CF3-C6H4	210	6.678
38	(1S,2R)	3,5-Cl2-C6H3	90	7.046
Phenyl piperidine analogues
Cpd	Stereochemistry	R	Ki (nM)	pKi
39	(1S,2R)		2.8	8.533
Piperazine analogues
Cpd	Stereochemistry	NR^1R^2	Ki (nM)	pKi
40	(1S,2R)		12	7.921

Ar, aromatic rings.

Figure 1. (A) Molecular alignment of the three most active compounds. Rings 1, 2, 3 and the carbonyl oxygen (acceptor) were used as superimposition sites as well as its 3D shapes. (B) Molecular alignment of all 40 studied compounds. (C) Chemical feature sites were employed as the template for the molecular alignment (distances are shown in Å).

Figure 2. PCA findings for the studied data set. (A) Factor selection, (B) scores plot, and (C) loadings values.

Figure 3. Distribution of test, training, and complete sets compounds according to pK_i range.

Table 2. Statistical parameters analysis of the three bests HQSAR models using fragment size default (4–7).

Model	Distinction	Fragment size	HL	N	q^2	SEP	r^2	SEE		RMSEP
29	A/B/H	1–4	61	6	0.638	0.487	0.898	0.259	0.416	0.353
41	A/B/C/H	1–4	71	6	0.659	0.472	0.905	0.250	0.500	0.437
42	A/B/C/H	2–5	151	6	0.644	0.483	0.910	0.243	0.592	0.416

HL, hologram length; N, optimal number of components; q², LOO cross-validated correlation coefficient; SEP, standard error of prediction; r², non-cross-validated correlation coefficient; SEE, standard error of estimate; , external validation correlation coefficient; RMSEP, root mean squared error of external prediction.

Table 3. Statistical data of the best CoMFA models.

	No focus	Focus1	Focus2	Focus3
w	–	0.30	0.30	0.50
d	–	1.00	1.50	1.00
q^2	0.632	0.758	0.695	0.810
SEV	0.491	0.397	0.442	0.351
N	6	6	6	6
r^2	0.946	0.944	0.925	0.929
SEE	0.188	0.192	0.219	0.216
S	0.366	0.411	0.434	0.359
E	0.634	0.589	0.566	0.641
	–	0.961	0.919	0.969
RMSEP	–	0.567	0.463	0.569

w, standard deviation weight factor; d, distance factor; q², LOO cross-validated correlation coefficient; SEV, standard error of validation; N, optimal number of components; r², non-cross-validated correlation coefficient; SEE, standard error of estimate; Field contribution: S, steric; E, electrostatic; , external validation correlation coefficient; RMSEP, root mean squared error of external prediction.

Table 4. Statistical data of the best CoMSIA models.

	E/H/D/A No focus	E/H/D/A w = 0.3; d = 1.0	S/E/H/D/A w = 0.3; d = 1.0	S/E/H/D/A w = 0.5; d = 1.0
q^2	0.639	0.679	0.691	0.684
SEV	0.486	0.442	0.426	0.439
N	6	4	3	4
R^2	0.903	0.907	0.911	0.908
SEE	0.252	0.246	0.242	0.246
S	–	–	0.089	0.090
E	0.349	0.340	0.317	0.314
H	0.248	0.222	0.211	0.200
D	0.222	0.214	0.174	0.177
A	0.180	0.223	0.210	0.219
	0.868	0.808	0.781	0.775
RMSEP	0.413	0.643	0.406	0.403

w, standard deviation weight factor; d, distance factor; q², cross-validation correlation coefficient; SEV, standard error of validation; N, optimal number of components; r², noncross-validation correlation coefficient; SEE, standard error of estimation. Field contribution: E, electrostatic; H, hydrophobic; , external validation correlation coefficient; RMSEP, root mean squared error of external prediction.

Figure 4. Plots of predicted versus experimental pK_i values HQSAR (A), CoMFA (B), and CoMSIA (C) best models.

Table 5. Experimental (Exp.) and predicted (Pred.) biological activities (pK_i) for the test set (Cpd).

		HQSAR		CoMFA		CoMSIA
Cpd	Exp. pKi	Pred. pKi	Residual	Pred. pKi	Residual	Pred. pKi	Residual
34	8.229	7.777	−0.452	8.094	0.135	8.266	−0.037
35	7.432	7.561	0.129	7.583	0.151	7.353	0.079
36	6.456	7.052	0.596	6.997	0.541	7.199	−0.743
37	6.678	5.985	−0.693	7.137	0.459	7.455	−0.777
38	7.046	7.955	0.909	7.254	0.208	7.911	−0.865
39	8.553	8.591	0.038	8.516	0.037	8.483	0.070
40	7.921	8.495	0.574	8.149	0.228	8.194	−0.273

Residual values are expressed in the last column for each model.

Figure 5. HQSAR contribution maps for the most potent antagonist (A) and the least active antagonist (B). Green and yellow correspond to positive contributions to activity. Red and orange correspond to negative contributions to activity. The colored maps are available in online version.

Figure 6. Energy contribution maps for the most (A) and the least active antagonists (B) generated for the best CoMFA model.

Figure 7. Steric and electrostatic contribution maps for the most (A) and the least active antagonists (B) generated for the best CoMSIA model.

Figure 8. Hydrophobic, H-bond donor, and H-bond acceptor contribution maps for the most (A) and the least active antagonists (B) generated for the best CoMSIA model.

Figure 9. Structure–activity relationship obtained from all QSAR models.