A REVIEW OF THE INTEGRATION OF AI IN MODERN DRUG DISCOVERY AND DEVELOPMENT

Drug discovery and development is going through a revolution with artificialintelligence (AI) which gives faster and cheaper solutions than traditionalmethods. AI-driven active learning is becoming more and more common indrug development as it finds important information inside large chemicalspaces even with very little labeled data. By screening compounds andimproving lead molecules, AI can find potential drug candidates by reducingthe time and resources needed to get from hit to lead. CADD has greatlyimpacted this field. Another advantage of using CADD with AI, ML, andDL to manage large biological data is the time and cost of drug developmenthas decreased. Deep learning and AI are necessary to organize and evaluatethe data generated by combinatorial chemistry. This helps de novo, virtualscreening, and structure-based drug design. Because active learning can finduseful information even with minimal labelled data it’s a great approach fordrug development. This approach addresses the challenges posed by data inthe broad realm of chemistry. The article delves into the role of learning indrug development across stages such as predicting compound interactions,conducting screenings, designing and optimizing molecules, and forecastingproperties. It also explores the obstacles and future potential of learning indrug development. Notably, structure-based drug design is gainingimportance as a method to accelerate and streamline lead discoverycompared to traditional methods. Moreover, artificial intelligence and deeplearning technologies are employed to analyze and organize data sets in thedrug discovery domain. Additionally, advanced machine learning toolsdriven by AI significantly influence the drug discovery process includingareas of medicinal chemistry.