2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

Table 6

Best MLR models for the prediction of antibacterial activity.

(a) Stepwise regression result for antibacterial activity against PA

Eq. number	Equation	𝑁	𝑟^2	CV 𝑟^2 (𝑞^2)	Std_error (𝑠)	Pred. 𝑟^2	Ran_𝑟^2	𝑍 score 𝑟^2
1	PAantibact = (0.8455) dipole + (0.0464) mol_MW + (0.0135) FISA − 7.5189	15	0.879	0.709	0.499	0.399	0.264	4.938
1.1	PAantibact = (1.0249) dipole + (0.0538) mol_MW − (0.0555) SASA + 21.7974	15	0.878	0.673	1.504	0.518	0.391	1.876

(b) Stepwise regression result for antibacterial activity against SA

Eq. number	Equation	𝑁	𝑟^2	CV 𝑟^2 (𝑞^2)	Std_error (𝑠)	Pred. 𝑟^2	Ran_𝑟^2	𝑍 score 𝑟^2
2	SAantibact = −0.2246 PISA − 0.0595 WPSA + 2.9410 EA (eV) + 107.5483	15	0.874	0.767	0.222	1.469	0.356	3.938
2.1	SAantibact = −(0.1343) PISA + (0.1660) PSA + (0.0494) FOSA + 61.1447	15	0.876	0.743	2.210	1.544	0.288	3.461