Background BACE-1 is an aspartate protease that is responsible for the proteolysis of amyloid precursor proteins (APP) into beta-amyloid (Aβ), a neurotoxic peptide in patients with Alzheimer’s disease (AD). As such, BACE-1 is a prime pharmacological target in the control of Aβ in the brain and its inhibition will be a sound approach in AD therapy. Methods The computational pipeline which comprised molecular docking (MD), Quantitative Structure Activity Relationship (QSAR) modelling and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) studies enabled the prediction of molecular interaction and relative inhibitory potentials of the hit compound. Results and Discussion The current study reports a naturally sourced small molecule inhibitor of BACE1 (C000000956) which was obtained through a computational pipeline. Also, pharmacological constraints such as pH dependent activity of the enzyme and blood brain barrier permeation which have been associated with the efficacy of previous BACE-1 inhibitors were well catered for. Our results suggest that orally delivered C000000956 is a potential small molecule inhibitor of BACE-1 which may find usefulness in AD-therapy. ^* This work was carried out at the Centre for Biocomputing and Drug Development, Adekunle Ajasin University, Akungba Akoko, Ondo state.

GSK3B has been an interesting drug target in the pharmaceutical industry. Its dysfunctional expression has prognostic significance in the top 3 cause of death associated with non-communicable diseases (cancer, Alzheimer?s disease and type 2 diabetes). Previous studies have shown clearly that inhibiting GSK3B has proven therapeutic significance in Alzheimer?s disease, but its contribution to various cancers has not been clearly resolved. In this study we report the contribution and prognostic significance of GSK3B to two breast cancer subtypes; ductal carcinoma in-situ (DCIS) and invasive ductal carcinoma (IDC) using the Oncomine platform. We performed high throughput screening using molecular docking, a well validated computational approach. We identified BT-000775, a compound which was subjected to further computational hit optimization protocols. Through computational predictions, BT-000775 is a highly selective GSK3B inhibitor, with superior binding affinity and robust ADME profiles suitable for the pathological presentations.

Background: Trypanosoma brucei (T. brucei) is the cause of the deadly human African trypanosomiasis (HAT) with a case fatality ratio of 10%. Objective: Targeting the essential Trypanosomal glucose metabolism pathway through the inhibition of phosphoglycerate kinase (PGK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a valid strategy for anti-T. brucei drug development. Methods: Here, quantitative structure activity relationship, molecular docking and microscopic studies were used to describe the mode of inhibition of selected compounds from the pathogen box PGK and GAPDH. Results: We identified 4 hit compounds from the pathogen box with optimal binding and chemical interactions. Notably, it was identified that interacting charge surface and atomic mass were key aspects of both PGK and GAPDH inhibition. Also, novel anti-trypanosomal compounds were identified from the pathogen box and their half maximal inhibitory concentrations were described. Conclusions: Our study presents new anti-trypanosomal compounds with optimal pharmacological profiles and an optimization strategy for improving target specificity in the rational design of novel anti-trypanosomal compounds.

Backgound: Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular descriptors (1, 2, & 3-D) from compounds targeting PBP2A in vivo. Methods: 37 (training set: 26, test set: 11) PBP2A-inhibitors were submitted for descriptor generation after which an unsupervised, non-exhaustive genetic algorithm (GA) was deployed for fishing out the best descriptor subset. Assignment of descriptors to a regression model was accomplished with the Partial Least Square (PLS) algorithm. At the end, an ensemble of 30 descriptors accurately predicted the ligand bioactivity, IC50 (R = 0.9996, R2 = 0.9992, R2a = 0.9949, SEE =, 0.2297 Q2LOO = 0.9741). Results & Discussion: Inferentially, we noticed that the overall efficacy of this model greatly depends on atomic polarizability and negative charge (electron) density. Besides the formula derived, the high dimensional model also offers critical insights into salient cheminformatics parameter to note during hit-to-lead PBP2A-antagonist optimization.