Weighted Network Analysis for Computer-Aided Drug Discovery

Biologically relevant chemical space is a set of bioactive compounds that can be candidates for drugs. In data-driven drug discovery, databases of bioactive compounds are explored. However, the biologically relevant chemical space is quite huge. Understanding the relationship between the structural similarity and the bioactivity closeness of compounds helps the efficient exploration of drug candidates. In these circumstances, network representations of the space of bioactive compounds have been suggested extensively. We define the weighted network where each node represents a bioactive compound, and the weight of each link equals the structural similarity between the compounds (nodes). We investigated the weighted network structure and how the bioactivity of compounds distributes on the network. We found that compounds with significantly high or low bioactivity have a stronger connection than those in the overall network.

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