Abstract
This study conducts a critical analysis of the state of artificial intelligence uses for drug discovery, highlighting important obstacles, unrealized potential, and tactical routes for successful adoption. The chapter emphasizes the noteworthy advancements in target identification, compound screening, and molecule design using AI-driven algorithms. It also covers the ethical ramifications that arise from the combination of AI and drug discovery, as well as the accessibility of AI models and regulatory issues. The significance of interdisciplinary collaborations, advances in computational power, and the creation of strong validation frameworks are emphasized as key strategies to address these problems. This study sheds light on the complex environment of AI in drug discovery by offering a thorough summary of the difficulties encountered, the opportunities that arise, and the strategic frameworks that are necessary to fully utilize AI's potential to transform the pharmaceutical sector.
Top1. Introduction
Artificial intelligence, refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence. These tasks can include problem-solving, learning, understanding natural languages, recognising patterns, making decisions, and more. AI systems are designed to mimic human cognitive functions and are capable of processing vast amounts of data to make predictions or take actions based on that data. AI technologies use various techniques, including machine learning, deep learning, natural language processing, computer vision, and more. Machine learning, in particular, involves training algorithms on data to recognise patterns and make predictions and decisions without being explicitly programmed for each specific task. Deep learning is a subset of machine learning that uses artificial neural networks to model and solve complex problems. AI has a wide range of applications across numerous fields, such as healthcare, finance, and autonomy, and is expected to have a profound impact on society, changing the way we work and live in the coming years (Roman-Naranjo, 2023).
Key Terms in this Chapter
Computer Aided Drug Design: Typically used in the field of drug discovery and development, computer-aided drug design (CADD) is the process of designing and finding new pharmaceutical compounds using computational techniques and tools. To comprehend the molecular interactions between possible therapeutic compounds and biological targets, computer modelling, experiments, and data analysis are used.
Artificial Intelligence (AI): The abbreviation AI is for “artificial intelligence.” It describes the artificial intelligence (AI) processes that machines, particularly computer systems, simulate. Artificial Intelligence (AI) is the umbrella term for a wide range of technologies and methodologies that allow computers to carry out tasks like learning, problem-solving, comprehending natural language, identifying sequences, and making decisions that normally require human intelligence.