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Zeroth and first order connectivity indices are employed to develop linear correlations for three physical properties of interest in pharmaceutical chemistry: octanol water partition coefficient (OWPC), melting point and water solubility.
With the aim of helping to decipher this higher order connectivity we propose a new methodology for gleaning patterns of interaction between biological processes, manifested by a significantly enriched web of protein-protein interactions, transcriptional coordination or genetic interactions.
We continue to consider the impact of higher order connectivity.
These new protein complexes are assembled according to the time order, connectivity and the overlapping of active proteins.
The first order connectivity that each viral protein has with the host proteins can be extracted directly from the data.
On average, the number of nodes having higher order connectivity is only 1.79% of the number of nodes having two incoming edges and two outgoing edges.
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With this approach the missing groups are created and their contributions are predicted using zero- and first-order connectivity indices.
Our results suggest thus that different mechanisms might be at play that structure the high-order connectivity features of scientific collaborations.
Hence, it would be beneficial to develop a language that encodes explicitly higher-order connectivity patterns and can distinguish them from sums of low order interactions.
In this work, second-order connectivity indices are used to obtain more information about steric features of the catalyst molecules, and non-linear correlations are employed to improve the accuracy of the property prediction equations.
The computational efficiency of Tabu search is not strongly dependent on the existence of non-linear constraints, so for solution using this method, a non-linear form for the second-order connectivity index was chosen, which decreases the number of binary variables required.
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