RESUMO

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

RESUMO

Cyclin-Dependent Kinase 2 (CDK2) and Vascular Endothelial Growth Factor Receptor (VEGFR2) have largely been considered as attractive targets for developing anticancer agents. However, there is no dual inhibitor commercially available in the market that interacts simultaneously with the allosteric back pocket of these enzymes. We applied a combined computational strategy that started with the generation of two overlapping pharmacophore models of both kinases at 'inactive' conformation. Next, several virtual libraries of natural products, including the databases TCM (Traditional Chinese Medicine), UEFS (Universidade Estadual de Feira de Santana), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products) and AfroDb (African Medicinal Plants Database) were deconstructed using a non-extensive version of the approach RECAP (retrosynthetic combinatorial analysis procedure). These natural-product-derived fragments (NPDFs) were screened and merged into drug-sized compounds, which were filtered by Lipinski's Rule-of-five (Ro5) and docking. As a result, two pharmacophore models, namely Hypo1 and Hypo2, were developed with an accuracy of 0.94 and 0.84, respectively. Deconstruction of natural products produced a set of 16655 unique non-extensive NPDFs that were screened against both pharmacophore models. Finally, after merging, Ro5-filtering and docking, we obtained a set of 20 hit compounds predicted to be diverse, developable, synthesizable and potent. The computational strategy proved successful to find virtual candidates of kinase inhibitors and therefore contributes to the identification of innovative multi-target compounds with potential anticancer activity. Communicated by Ramaswamy H. Sarma.

Assuntos

RESUMO

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure-activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied "single molecule-single scaffold" correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure-property relationships, and identification of analog series and ASBS. The molecule-core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure-property relationships analyses.