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Author: search.filters.author.AGBI, MayowaSubject: search.filters.subject.Bioinformatics

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    A COMPUTATIONAL FRAMEWORK FOR PREDICTING COMPOUNDPROTEIN INTERACTION FOR PROSTATE CANCER THERAPEUTIC DISCOVERY
    (Covenant University Ota, 2025-08) AGBI, Mayowa; Covenant University Dissertation
    Prostate cancer (PCa) is a major public health issue globally. In sub-Saharan Africa, with its limited number of diagnostic and treatment resources, it accounts for high mortality. The conventional approach to drug discovery is lengthy, expensive, and often insufficient to address the complex treatment-resistant prostate cancers present. In this study, a deep learning computational framework to predict Compound-Protein Interactions (CPI) for prostate cancer drug discovery was developed. An end-to-end machine learning pipeline was implemented using curated datasets from Zenodo, ChEMBL, BindingDB, and UniProt. Molecular representations for compounds were constructed using 2048-bit Morgan fingerprints, dimensionally reduced to 200 via Principal Component Analysis (PCA), and for the proteins, 100-dimensional 3-mer Word2Vec embeddings were used. These features were fed into a double-input deep neural network that was optimized with binary-cross-entropy loss, the Adam optimizer, and dropout regularization. The model identified five novel bioactive compounds for targeting proteins of prostate cancer biomarkers. Model confidence was used to prioritize predicted interactions for AR, SRC, and EGFR. Molecular docking in PyRx and AutoDock Vina, followed by visualization in Discovery Studio supporting strong binding affinity (-7.2 to -10) and complementarity from the structural point of view, constituting therapeutic potential. An integration of molecular docking enriched translational value to the prediction. The results presented here point to a disease-specific platform for in silico drug discovery in prostate cancer. This study opens a very promising path toward giving priority to candidate compounds by coupling the deep learning with structure-based affirmation. It provides a very viable ground to be merged with experimental validation and combinatorial therapy design, thereby taking one step further into machine learning-assisted precision oncology.