Role of AI and Machine Learning in Designing Next-Generation Therapeutics

In ways that were previously unthinkable, the new era of artificial intelligence (AI) and machine learning (ML) is revolutionizing drug discovery and therapeutic development. It offers previously unheard-of chances to create the upcoming generation of treatments with greater accuracy and effectiveness. This chapter discusses how AI/ML techniques are used in important phases of therapeutic design such as target identification, drug repurposing, drug efficacy optimization, and adverse effect minimization. Distinct ways that AI/ML are employed in key aspects of therapeutic designs, such as target discovery drug repositioning, drug efficacy optimization, and limiting side effects, are discussed in this chapter. The ability of AI/ML to interpret large biological datasets, anticipate drug interactions, and simulate the behaviour of molecules using advanced algorithms can hasten the transfer from research to clinical usage. Inventive AI-driven techniques including deep learning models for predicting protein-ligand binding generative adversarial networks (GANs) for developing new compounds and natural language processing (NLP) for biological literature survey is also included in this chapter. Also, for critical concerns, it provides distinct methods to resolve data biases algorithm understandability and ethical dilemmas. With an effect on the real-world case studies, this chapter analyses all aspects of how AI/ML impacts on therapeutic innovations towards precision cancer and personalized drugs. This chapter aims to fill the gap between that understanding and the clinicians and researchers seeking to completely exploit AI/ML for life saving drugs by combining clinical effects.