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Multi-target QSPR assemble of a Complex Network for the distribution of chemicals to biphasic systems and biological tissues

Abstract:

Chemometrics, that based pbkp_rediction on the probability of chemical distribution to different systems, is highly important for physicochemical, environmental, and life sciences. However, the amount of information is huge and difficult to analyze. A multi-system partition Complex Network (MSP-CN) may be very useful in this sense. We define MSP-CNs as large graphs composed by nodes (chemicals) interconnected by arcs if a pair of chemicals have similar partition in a given system. Experimental quantification of partition in many systems is expensive, so we can use a Quantitative Structure-Partition Relationship (QSPR) model. Unfortunately, with classic QSPR we need to use one model for each system. Here construct the first MSP-CN based on a multi-target QSPR (mt-QSPR). The model is based on the spectral moments (πk) of a molecular Markov matrix weighted with atomic parameters that depend on both the nature of the atom and the partition system. The mt-QSPR pbkp_redicts 90.6% of 413 compound/system pairs in training series and 90.0% in validation. The MSP-CN pbkp_redicted presents 413 nodes, 2060 edges, average node degree 9.9, and only 7.7% drugs are unconnected. The model was used to study the biophysical phenomena of transport or distribution of G1 (a novel antimicrobial drug) to different rat tissues. Pbkp_redicted probabilities (P) coincide with low experimental partition coefficients (logPC) reported herein by the first time in skin (P = 0.455; logPC = - 0.02 < 0 → U), heart (0.453; - 0.02 → U), and brain (0.324; - 0.34 → U). The Kamada-Kawai algorithm evidenced the community structure of the MSP-CN and clusters G1 into three different communities of the U-type drugs. These results coincide with the low distribution of G1 to these tissues and consequently have low expected drug side effect. © 2008.