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    Role of Artificial Intelligence in Teaching and Learning Chemical Sciences
    (Bentham Science Publishers Pte. Ltd. Singapore, 2024) Shahla Tanveer, Mariyam Tanveer, Ayesha Tanveer
    Artificial Intelligence (AI) is revolutionizing our everyday tasks, and education has certainly not been left behind. AI harnesses technologies such as machine learning, natural language processing, and deep learning, to execute tasks and elevate our problem-solving capabilities. The infinite possibilities that arise due to interactions between atoms and molecules further leading to bond formation are nearly impossible for a human to comprehend. Thus, AI is playing a vital role in understanding chemistry by accelerating research, designing novel molecules, and optimizing processes. AI plays a diverse role, from assisting in drug discovery research to identifying new drug targets to supporting personalized learning experiences that aid students in their learning journeys. AI-powered adaptive learning system identifies a student’s performance and tailor the learning requirements accordingly. Students receive real-time feedback and personalised content helping them to understand the concepts more easily. AI is being used to develop interactive simulations and customized learning programs to help students learn chemistry more efficiently. Virtual laboratories driven by AI provide a safe and reachable environment for hands-on experience. This allows students to be inquisitive about chemical reactions, molecular structures, and their spectroscopic analysis in a risk-free environment. Some examples include Chat GPT, which helps create a customized learning experience for students while helping them answer their queries, an AI-powered tutoring system known as Socratic, which helps the students learn chemistry concepts, and Molecules in Motion (an AI-powered simulation) to inspect the behaviour of molecules. This chapter discusses how the union of AI and chemical sciences has accelerated innovation in the field of chemistry and can further improve learning outcomes.
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    Machine Learning Approaches to Rational Drug Design
    (Springer, 2020) Salman Akhtar, Mohammad Kalim Ahmad Khan, Khwaja Osama
    Pharmaceutical industries are multibillionaire setups with a diligent team ofscientists, researchers, technical manpower, and investors. A major concern ofsuch industries is to always curtail the time and cost factor associated with them.Bioinformatics involving machine learning (ML) methods have come to theforefront to address this problem. The predictive and statistical efficacy of MLmethodologies has even proven to propose better leads than a wet lab pipeline.This chapter aims to give a brief overview of underlying principles of mainly GAsand ANNs as popular ML algorithms and deeper insight into their robustapplications in the field of modern day drug design. It also attempts to share thefuture prospects of such ML techniques and their limitations with possiblesolutions hereafter.