Prediction of bioactivity of bioactive compounds from kelulut honey using naïve bayesian (nb) modelling / Nur Afra Asyeerah Binti Musinon@musinun

Faculty of Chemical and Process Engineering Technology

Final Year Report (Bachelor of Manufacturing Engineering Technology (Pharmaceuticals) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025

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This investigation analysis will be done for auto-immune Type 1 Diabetes (T1D) with FasL as the target protein using the compounds from Kelulut honey (stingless bee honey). T1D happened due to the interaction of the Fas-FasL that lead to the apoptosis (cell death programme) of β-cells in the liver that cannot induce the insulin to control the glucose in the bloodstream. Thus, we have determined the bioactivity of the compounds of Kelulut honey to FasL protein as our target protein. The compounds have been compared using half-maximal inhibitory concentration (IC50) and inhibitory constant (Ki) by implementing the Naïve Bayesian modelling in the MATLAB. Active ligands derived from Naïve Bayesian modelling then undergo the chemical and biological clustering using K-mean clustering and cosine similarity was done to know the relationship between biological and chemical clustering. After that, molecular docking was performed between protein-ligand to get the binding affinity, where the predicted active ligands were compared with the highest binding affinity of the compounds. Finally, we are able to predict the Kelulut honey compounds that have the therapeutic potential and describe the chemical and biological properties needed to bind with FasL to prevent apoptosis β-cells and mediate therapeutic effects.