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Table 4 Topological indices based training QSAR model and interpretation of the modeled descriptors

From: In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors

Validated training QSAR model

\( \begin{aligned} {\text{pIC5}}0 & = \left( { - 10. 2 3 4} \right) \, + \, \left( { - 9. 3} \right)*{\text{BIC5 }} + \, \left( { - 1. 8 3} \right)*{\text{GATS7e }} + \, \left( { - 3. 1 9} \right)*{\text{MATS7e }} + \, \left( { 4. 8 1} \right)*{\text{BEHe4 }} \\ & \quad + \, \left( { - 1. 2 1} \right)*{\text{EEig12r }} + \, \left( { - 0.0 6 5} \right)*{\text{DP12 }} + \, \left( { 1. 8 9} \right)*{\text{BELm7 }} + \, \left( { 3. 1} \right)*{\text{PCR}} \\ \end{aligned} \)

\( \begin{aligned} {\text{N}} = 50,\, {\text{R}}^{ 2} = 0. 870, \quad {\text{Q}}^{ 2} = 0.810,\quad {\text{Pred}}\_{\text{R2 }} = 0. 7 3 7,\quad {\text{r}}_{\text{m}}^{ 2} = 0. 6 5 9, \hfill \\ {\text{Average r}}^{ 2}_{\text{m}} \left( {\overline{{{\text r}_{\text{m}}^{2} }} } \right) \, = \, 0. 6 8 2,\quad {\text{Delta r}}^{ 2}_{\text{m}} \left( \Delta {\text{r}}^{ 2_{\text{m}} } \right) = 0.0 4,\quad {\text{SEE}} = 0. 2 8 2 \end{aligned} \)

Modeled descriptors

Interpretation

BIC5

Bond information content index (neighborhood symmetry of 5-order)

GATS7e

Geary autocorrelation—lag 7/weighted by atomic Sanderson electronegativities. 2D autocorrelations

MATS7e

Moran autocorrelation—lag 7/weighted by atomic Sanderson electronegativities

BEHe4

Highest eigen value of number 4 of burden matrix/weighted by atomic Sanderson electronegativities

EEig12r

Eigenvalue 12 from edge adj. matrix weighted by resonance integrals

DP12

Molecular profile number 12

BELm7

Lowest eigenvalue number 7 of Burden matrix/weighted by atomic masses

PCR

ratio of multiple path count over path count