ARTIFICIAL INTELLIGENCE: RECENT ADVANCEMENTSIN DRUG DESIGN AND DEVELOPMENT
Ram Babu Sharma, Swati Kaushal, Amardeep Kaur, Divya Dhawal Bhndari · 2023
The convergence of Artificial Intelligence (AI) and drug design has ushered in a new era of innovation, significantly transforming the landscape of pharmaceutical research and development. Recent breakthroughs in AI methodologies, such as machine learning and deep learning, have empowered researchers to navigate the complex and vast chemical space with unprecedented efficiency. This abstract provides a succinct overview of the latest advancements in AI-driven drug design and development. One notable area of progress lies in virtual screening, where AI algorithms can predict the binding affinity of potential drug candidates to specific targets, expediting the identification of promising compounds. Additionally, generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), have enabled the de novo design of novel molecules, offering a creative approach to drug discovery. Furthermore, AI is increasingly employed in predicting drug toxicity, optimizing pharmacokinetics, and unraveling complex biological pathways. The integration of multi-omics data and the development of explainable AI models enhance our understanding of drug mechanisms and facilitate more informed decision-making in the drug development pipeline.As AI continues to evolve, collaborative efforts between computational scientists, biologists, and chemists have become paramount. The synergy of expertise ensures a holistic approach to drug design, leveraging the strengths of both AI and traditional methods. This abstract encapsulates the recent strides in AI applications for drug discovery, underscoring the transformative impact on the efficiency and success rates of drug development processes.