Bioinformatics and machine learning in prevention, detection and treatment of HIV/AIDS

Abstract

As the acquired immunodeficiency syndrome (AIDS) pandemic continues to be a major health crisis of global concern, new strategies in the management and treatment of the disease is being explored. This project titled “Bioinformatics and Machine Learning in Prevention, Detection and Treatment of HIV/AIDS” discusses the existing processes and procedures within which computational (Bioinformatics and Machine Learning) techniques and approaches that can be potentially applied in the global fight to end the HIV/AIDS pandemic e.g. homology modeling, virtual screening, Quantity Structural Activity Relationship (QSAR) and molecular docking. It further reviews the bioinformatics and various machine learning techniques such as Support Vector Machine (SVM), Decision Tree Algorithms and Artificial Neural Networks (ANNs) that are incorporated into computational tools (Computer-Aided Drug Design-CADD) to accelerate the process of drug design and development of anti-HIV drugs by reviewing distinguished journals, articles and databases. Attempts were taken to identify gaps within the existing literature.