Design and Implementation of an AI‐Based Platform for Drug Discovery in HIV Treatment: A Case Study of Federal University of Technology, Minna, Niger State
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Background of the Study
The persistent global challenge of HIV/AIDS demands innovative therapeutic solutions, and drug discovery remains a critical area of research. Advances in artificial intelligence (AI) have opened new avenues in pharmaceutical research, particularly in predicting molecular interactions and optimizing candidate compounds. At Federal University of Technology, Minna, researchers are harnessing AI techniques such as deep learning and neural networks to develop a platform that accelerates the identification of novel drug candidates for HIV treatment. This platform integrates high-throughput screening data with predictive algorithms to simulate compound-protein interactions, thereby facilitating the identification of promising molecules with potential anti-HIV activity (Adebayo, 2023). The system’s design emphasizes a modular architecture that allows for continuous learning as more data become available. By automating the drug discovery process, the platform aims to reduce both the time and cost associated with traditional methods. Moreover, the use of cloud computing ensures scalability, enabling real-time analysis of massive datasets and providing researchers with dynamic feedback for iterative optimization (Ibrahim, 2024). This approach also incorporates mechanisms for error correction and validation through cross-referencing with global databases. In addition, interdisciplinary collaboration among computer scientists, pharmacologists, and bioinformaticians has led to the development of robust algorithms that capture complex biochemical interactions. Ultimately, the platform aspires to transform the conventional drug discovery paradigm by leveraging AI to pinpoint compounds with high binding affinity to HIV targets, thereby contributing to the development of more effective therapies. This research not only addresses the current inefficiencies in drug discovery but also supports personalized treatment strategies by correlating genetic factors with drug responsiveness (Chukwu, 2024).
Statement of the Problem
Despite significant progress in HIV research, the discovery of effective drugs remains encumbered by lengthy experimental processes and high costs. Traditional drug discovery pipelines are hindered by manual data analysis and a lack of predictive accuracy in identifying promising compounds. At Federal University of Technology, Minna, the absence of an integrated, AI‐based platform has led to fragmented research efforts and inconsistent outcomes in drug candidate screening (Bello, 2023). Current methodologies struggle with the high dimensionality of biochemical data and are often unable to model the complex interactions between potential drugs and HIV proteins accurately. Additionally, there is a notable gap in the integration of heterogeneous data sources, such as chemical libraries and clinical trial outcomes, which further impairs the identification of effective treatments. These challenges necessitate a system that can automate and standardize the process while continuously learning from new data. The proposed platform seeks to bridge this gap by employing state-of-the-art deep learning techniques to enhance predictive accuracy and streamline the drug discovery process. Addressing these limitations is essential for reducing development timelines and facilitating the translation of research findings into clinically viable therapies. Such an approach will not only improve research efficiency but also contribute to the global fight against HIV/AIDS by enabling rapid discovery and optimization of anti-HIV compounds (Okeke, 2024).
Objectives of the Study
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To design an AI‐based platform for the prediction of anti-HIV drug candidates.
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To implement and validate the platform using high-throughput screening datasets.
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To assess the platform’s effectiveness in reducing drug discovery timelines and costs.
Research Questions
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How can AI algorithms be optimized for predicting anti-HIV drug candidates?
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What improvements in processing speed and predictive accuracy can the platform achieve compared to traditional methods?
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How can the platform be integrated into existing drug discovery workflows in HIV research?
Significance of the Study
This study is significant as it pioneers an AI‐driven approach to drug discovery in HIV treatment, promising to accelerate the identification of effective compounds while reducing costs and development times. The platform’s success could lead to more efficient therapeutic discovery processes and support personalized medicine strategies. The findings will benefit both academic research and the pharmaceutical industry, contributing to global efforts in combating HIV/AIDS (Adebayo, 2023).
Scope and Limitations of the Study
The study is limited to the design, implementation, and evaluation of the AI‐based platform at Federal University of Technology, Minna, focusing exclusively on anti-HIV drug discovery. It does not extend to clinical trials or other disease targets.
Definitions of Terms
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Drug Discovery: The process of identifying new candidate medications.
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Artificial Intelligence (AI): Computer systems that perform tasks normally requiring human intelligence.
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High-Throughput Screening: A method used to quickly conduct millions of chemical, genetic, or pharmacological tests.
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