Structural diversity of biologically interesting datasets: a scaffold analysis approach

Abstract

BackgroundThe recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets.ResultsIn this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.ConclusionsCurrently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.

AfroDb: A Select Highly Potent and Diverse Natural Product Library from African Medicinal Plants

“…This observation could be explained by the complexity and large sizes of some of the compounds within the natural product library. The large proportion of very large and complex NPs in AfroDb, could also explain the average molar weight (Da), when compared to those of the standard “drug-like”, “lead-like” libraries and typical drugs (Da for typical drugs) [41]. This same explanation holds for the trend which is observed in the distributions of log P , HBD, NCC, NO, NRB, NR, TPSA and HBA for AfroDb, when compared with the ChemBridge dataset.…”

A systems approach to traditional oriental medicine

“…We note that one report has shown that natural products have greater similarities to chemical drugs than human metabolites in terms of Tanimoto similarity (0.85 versus 0.58) 17 . A reason for this difference may be the use of compounds from the ZINC NP database 27 , which has information on only commercially available compounds.…”

“…Although natural products have been subjected to this type of large-scale comprehensive chemical analyses before [15][16][17][18] , more detailed analyses are needed at the systems level. We also review design principles of We categorized the effects of combinations of TOM compounds into four types of synergistic combinations 25,36 ( Fig.…”

StreptomeDB: a resource for natural compounds isolated from Streptomyces species

“…Diverse compound classes such as anthracyclines and cyclic peptides are included (Figure 1). The large proportion of very complex compounds results in a much higher average molecular weight (median: 453 g/mol) compared with typical drugs (median: 310 g/mol, 19). For example, the approved antibiotic drug actinomycin has a molecular weight of 1255 g/mol.…”