Inverse-QSPR/QSAR aims to solve the inverse problem of chemical structure generation based on QSPR/QSAR models, once the properties or activities are specified. To efficiently solve this problem, an exhaustive ring-system-based structure generation methodology was developed. The concept of the applicability domain (AD) is automatically acknowledged within the proposed strategy. The local AD is considered by introducing the probability distribution of a given data set, and the universal AD is considered using ring-system-based fragments in the training data set. Structures with desired properties or activities are enumerated by assembling fragments, including atomic elements, in a tree-like way. The usefulness of the proposed method is demonstrated through a case study of ligand design for the human alpha 2A adrenergic receptor (ADR2A_HUMAN). We succeeded in generating structures focusing only on a pre-defined region in chemical space, resulting in structures whose desired activity has a high likelihood being efficiently generated. In addition, the limitations of our proposed method and future challenges are discussed.
Keywords: Chemoinformatics; Drug design; Inverse QSPR/QSAR; Structure generation.
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