Table 9.
Regression analysis and quality of correlation for modeling antibacterial and antifungal activity of synthesized Benzimidazole derivatives.
S. No. | QSAR model | n | r | q2 | s | F |
---|---|---|---|---|---|---|
B. subtilis | ||||||
1 | pMICbs = −1.383J + 3.959 | 20 | 0.610 | 0.269 | 0.164 | 10.66 |
E. coli | ||||||
2 | pMICec = −0.112 2χv + 3.034 | 20 | 0.528 | 0.108 | 0.247 | 6.97 |
3 | pMICec = −0.224 2χv + 2.622J + 7.559 | 20 | 0.823 | 0.558 | 0.170 | 17.90 |
C. albicans | ||||||
4 | pMICca = −0.923 LUMO + 0.302 | 20 | 0.656 | 0.237 | 0.157 | 13.60 |
A. niger | ||||||
5 | pMICan = 0.00002 NE + 0.556 | 20 | 0.636 | 0.314 | 0.177 | 12.28 |
Antifungal activity | ||||||
6 | pMICaf = −1.319J + 3.257 | 20 | 0.629 | 0.278 | 0.149 | 11.81 |
Antimicrobial activity | ||||||
7 | pMICam = −1.244J + 3.535 | 20 | 0.707 | 0.419 | 0.113 | 18.03 |
8 | pMICam = −1.370 J–0.568 LUMO + 2.869 | 20 | 0.877 | 0.705 | 0.079 | 28.60 |
9 | pMICam = −0.045 2χv – 1.680 LUMO + 4.503 | 20 | 0.767 | 0.456 | 0.106 | 12.15 |