Computational chemistry methods can significantly reduce experimental costs in early stages of a drug development project by filtering out unsuitable candidates and discovering new chemical matter. Molecular alignment is a key pre-requisite for 3D similarity evaluation between compounds and pharmacophore elucidation. Relying on the hypothesis that the variation in maximal achievable binding affinity for an optimized drug-like molecule is largely due to desolvation, we explore herein a novel small molecule 3D alignment strategy that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond donor/acceptor groups in each compound. A brief description of the method, as implemented in the software package PharmScreen, is presented. The computational procedure is calibrated by using a dataset of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the CCDC AstraZeneca test set of 121 experimentally derived molecular overlays. The results confirm the suitability of MST based-hydrophobic parameters for generating molecular overlays with correct predictions obtained for 100%, 93%, and 55% of the molecules classified into easy, moderate and hard sets, respectively. The potential of this tool in a drug discovery campaign is then evaluated in a retrospective study with the aim to evaluate the correlations between activities and similarity score of a series of sigma-1 receptor ligands. The results confirm the suitability of the tool for Drug Discovery purposes finding the 67% of the most active ligands (≤10 nM) in Q1 of the ranking and the most active compound in position five.
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