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Table 2 The definition of the used molecular descriptors for modelling of two kinds of activities (3D7 and W2)

From: Quantitative structure–activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks

Molecular descriptors Definition Descriptor category Strain
GATS4m Geary autocorrelation of lag 4 weighted by mass 2D autocorrelations 3D7
GATS7m Geary autocorrelation of lag 7 weighted by mass 2D autocorrelations 3D7
Mor06u Signal 06/unweighted 3D-MoRSE descriptors 3D7
Mor31u 3D-MoRSE, signal 31/unweighted 3D-MoRSE descriptors 3D7
+R3e R maximal autocorrelation of lag 3/weighted by Sanderson electronegativity GETAWAY descriptors 3D7
+R2p R maximal autocorrelation of lag 2/weighted by polarizability GETAWAY descriptors 3D7
BEHm3 Highest eigenvalue n.3 of Burden matrix/weighted by atomic masses Burden eigenvalues W2
MATS7m Moran autocorrelation of lag 7 weighted by mass 2D autocorrelations W2
RDF020m Radial distribution function-020/weighted by mass RDF descriptors W2
Mor23u 3D-MoRSE signal 23/unweighted 3D-MoRSE descriptors W2
Mor20p 3D-Morse signal 23/weighted by polarizability 3D-MoRSE descriptors W2
MLOGP Moriguchi octanol–water partition coefficient Molecular properties W2