Abstract
Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 DTP compounds tested in the NCI60 tumor cell line anticancer drug screen were computed using chemical hashed fingerprints and 3D molecule shapes to calculate 2D and 3D similarities, respectively. Additionally, pairwise biological activity similarities were calculated by correlating the 60 element vectors of pGI50 values corresponding to the cytotoxicity of the compounds across the NCI60 panel. Subsequently, we compared the power of 2D and 3D structural similarity metrics to predict the toxicity pattern of compounds. We found that while the positive predictive value and sensitivity of 3D and molecular descriptor based approaches to predict biological activity are similar, a subset of molecule pairs yielded contradictory results. By simultaneously requiring similarity of biological activities and 3D shapes, and dissimilarity of molecular descriptor based comparisons, we identify pairs of scaffold hopping candidates displaying characteristic core structural changes such as heteroatom/heterocycle change and ring closure. Attempts to discover scaffold hopping candidates of mitoxantrone recovered known Topoisomerase II (Top2) inhibitors, and also predicted new, previously unknown chemotypes possessing in vitro Top2 inhibitory activity.
Highlights
Drug resistance poses a serious challenge in the treatment of malignant diseases or bacterial infections, prompting the need for the development of new drugs
Relation of structural similarity metrics and biological activity of the Developmental Therapeutics Program (DTP) compounds In order to assess the relation of structural similarities to biological activity, we calculated pairwise molecular descriptor similarities, 3D shape similarities (ROCS) and biological activity (BiolAct or BA) similarities among 4858 compounds analyzed by DTP’s NCI60 screening project [7]
We find that while an increase of the threshold of a structural similarity metric increases the positive predictive value, it results in a decrease of sensitivity
Summary
Drug resistance poses a serious challenge in the treatment of malignant diseases or bacterial infections, prompting the need for the development of new drugs. With the increased understanding of the genetic addictions, dependencies and vulnerabilities of cancer cells, target based approaches have yielded several successful treatment options, such as in the case of drugs developed against the epidermal growth factor receptor (reviewed in [1]). A significant number of novel FDA approved drugs across all therapeutic areas [2] and in cancer [3] have been identified by phenotypic screens. Target and ligand based approaches are widely used in virtual drug design. Opposed to target-based design, where drug binding to a known target is tested [4], ligand-based screening can be utilized when the three dimensional (3D) structure of the target protein is not available [5]. Advances in computational techniques and hardware solutions have enabled in silico methods, in particular virtual screening, to accelerate lead identification and optimization [6]
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