Partitioning algorithms are described that operate in chemical reference spaces formed by combinations of binary-transformed molecular descriptors and aim at the identification of potent hits in ligand-based virtual screening. One of these approaches depends on mapping of consensus positions of compound activity sets in descriptor spaces followed by step-wise extension of the dimensionality of these spaces and re-mapping of activity-dependent consensus positions. Dimension extension is carried out to increase the discriminatory power of descriptor combinations and distinguish database compounds from potential hits. This method was originally named Dynamic Mapping of Consensus positions (DMC) and subsequently extended in order to take different potency levels of known active molecules into account and increase the probability of recognizing potent database hits. The extension was accomplished by adding potency scaling to DMC calculations, and the resulting approach was termed POT-DMC. Results of comparisons of DMC and POT-DMC calculations on different classes of active compounds with substantially varying potency levels support the validity of the POT-DMC approach.
Keywords: high-throughput screening (hts), pharmacophore, molecular similarity-based methodologies, algorithms, cell-based techniques, recursive median partitioning, dynamic mapping, tyrosine kinase
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