Abstract
Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. We assess the bioactivity and “drug-likeness” of a relatively small but structurally diverse dataset (containing >1,000 compounds) from African medicinal plants, which have been tested and proven a wide range of biological activities. The geographical regions of collection of the medicinal plants cover the entire continent of Africa, based on data from literature sources and information from traditional healers. For each isolated compound, the three dimensional (3D) structure has been used to calculate physico-chemical properties used in the prediction of oral bioavailability on the basis of Lipinski’s “Rule of Five”. A comparative analysis has been carried out with the “drug-like”, “lead-like”, and “fragment-like” subsets, as well as with the Dictionary of Natural Products. A diversity analysis has been carried out in comparison with the ChemBridge diverse database. Furthermore, descriptors related to absorption, distribution, metabolism, excretion and toxicity (ADMET) have been used to predict the pharmacokinetic profile of the compounds within the dataset. Our results prove that drug discovery, beginning with natural products from the African flora, could be highly promising. The 3D structures are available and could be useful for virtual screening and natural product lead generation programs.
Highlights
Drug design and discovery efforts have often resorted to natural sources for hit/lead compound identification [1,2,3,4,5]
The use of computer modeling in drug discovery otherwise known as computer-aided drug design (CADD) requires a compound library containing 3D structures of potential leads, which need to be screened in silico, with the view of identifying hit compounds
The number of rotatable bonds (NRB) within the AfroDb library was used as an additional criterion to test for the favourable drug metabolism and pharmacokinetics (DMPK) outcomes
Summary
Drug design and discovery efforts have often resorted to natural sources for hit/lead compound identification [1,2,3,4,5]. Modern drug discovery efforts incorporate computer-aided approaches like ligand docking, pharmacophore searching, neural networking and binding free energy calculations of potential drugs towards a target receptor The rationale behind such in silico methods has been to simulate the interaction between a potential drug molecule and its receptor or binding site (often the drug target) using 3D computer models [13,14]. The use of computer modeling in drug discovery otherwise known as computer-aided drug design (CADD) requires a compound library containing 3D structures of potential leads, which need to be screened in silico, with the view of identifying hit compounds If this effort is successful, the identified hits could be confirmed as active compounds using screening assays. Such a procedure considerably cuts down the cost of drug discovery and development [15]
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