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Artificial Intelligence And Machine Learning In Drug Design And Development

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Artificial Intelligence And Machine Learning In Drug Design And Development

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Author : Abhirup Khanna
language : en
Publisher: John Wiley & Sons
Release Date : 2024-06-21

Artificial Intelligence And Machine Learning In Drug Design And Development written by Abhirup Khanna and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-21 with Computers categories.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Artificial Intelligence And Machine Learning In Drug Design And Development

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Author : Abhirup Khanna
language : en
Publisher: John Wiley & Sons
Release Date : 2024-07-18

Deep Learning In Drug Design

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Author : Qifeng Bai
language : en
Publisher: Academic Press
Release Date : 2025-10-01

Deep Learning In Drug Design written by Qifeng Bai and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-01 with Medical categories.

Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, applications, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models for drugs, and the deep learning applications in various aspects of drug design. This book will give readers an intuitive and simple understanding of the encoding and decoding of drugs for model training, while deep learning methods profile the different training perspectives for drug design including sequence-based, 2D, and 3D drug design based on geometric deep learning. This book is suitable for readers who are seeking to learn and use deep learning methods and applications for drug discovery and other related fields. Deep Learning in Drug Design: Methods and Applications is particularly helpful to graduate students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented. - Introduces the basic theories, current methods, and cases of deep learning for drug design - Presents the major application fields of drug design based on deep learning including protein folding, retrosynthesis prediction, molecular docking, and ADMET prediction, among others - Explains the artificial intelligence of deep learning for drug design models

Drug Design Using Machine Learning

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Author : Inamuddin
language : en
Publisher: John Wiley & Sons
Release Date : 2022-11-22

Drug Design Using Machine Learning written by Inamuddin and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-22 with Medical categories.

DRUG DESIGN USING MACHINE LEARNING The use of machine learning algorithms in drug discovery has accelerated in recent years and this book provides an in-depth overview of the still-evolving field. The objective of this book is to bring together several chapters that function as an overview of the use of machine learning and artificial intelligence applied to drug development. The initial chapters discuss drug-target interactions through machine learning for improving drug delivery, healthcare, and medical systems. Further chapters also provide topics on drug repurposing through machine learning, drug designing, and ultimately discuss drug combinations prescribed for patients with multiple or complex ailments. This excellent overview Provides a broad synopsis of machine learning and artificial intelligence applications to the advancement of drugs; Details the use of molecular recognition for drug development through various mathematical models; Highlights classical as well as machine learning-based approaches to study target-drug interactions in the field of drug discovery; Explores computer-aided technics for prediction of drug effectiveness and toxicity. Audience The book will be useful for information technology professionals, pharmaceutical industry workers, engineers, university researchers, medical practitioners, and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements.

Computer Aided And Machine Learning Driven Drug Design

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Author : Vinícius Gonçalves Maltarollo
language : en
Publisher: Springer Nature
Release Date : 2025-02-27

Computer Aided And Machine Learning Driven Drug Design written by Vinícius Gonçalves Maltarollo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Science categories.

The computer-aided drug design research field comprises several different knowledge areas, and often, researchers are only familiar or experienced with a small fraction of them. Indeed, pharmaceutical industries and large academic groups rely on a broad range of professionals, including chemists, biologists, pharmacists, and computer scientists. In this sense, it is difficult to be an expert in every single CADD approach. Furthermore, there are well-established methods that are constantly revisited, and novel approaches are introduced, such as machine-learning based scoring functions for molecular docking. This book provides an organized update of the most commonly employed CADD techniques, as well as successful examples of actual applications to develop bioactive compounds/drug candidates. Also includes is a section of case studies that cover certain pharmacological/target classes, focusing on the applications of the previously described methods. This part will especially appeal to professionals who are not as interested in the theoretical aspects of CADD. This is an ideal book for students, researchers, and industry professionals in the fields of pharmacy, chemistry, biology, bioinformatics, computer sciences, and medicine who are seeking a go-to reference on drug design and medicinal chemistry.

The Era Of Artificial Intelligence Machine Learning And Data Science In The Pharmaceutical Industry

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Author : Stephanie K. Ashenden
language : en
Publisher: Academic Press
Release Date : 2021-04-23

The Era Of Artificial Intelligence Machine Learning And Data Science In The Pharmaceutical Industry written by Stephanie K. Ashenden and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-23 with Computers categories.

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

Deep Learning In Personalized Healthcare And Decision Support

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Author : Harish Garg
language : en
Publisher: Elsevier
Release Date : 2023-07-20

Deep Learning In Personalized Healthcare And Decision Support written by Harish Garg and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-20 with Science categories.

Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. - Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management - Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way - Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies

Explainable Artificial Intelligence For Biomedical Applications

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Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2023-12-14

Explainable Artificial Intelligence For Biomedical Applications written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-14 with Technology & Engineering categories.

Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today’s intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI). This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights. Topics discussed in the book include: XAI for the applications with medical images XAI use cases for alternative medical data/task Different XAI methods for biomedical applications Reviews for the XAI research for critical biomedical problems. Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes.

Artificial Intelligence In Pharmacy Applications Challenges And Future Directions In Drug Discovery Development And Healthcare

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Author : Sarika Patil
language : en
Publisher: Deep Science Publishing
Release Date : 2025-08-08

Artificial Intelligence In Pharmacy Applications Challenges And Future Directions In Drug Discovery Development And Healthcare written by Sarika Patil and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-08 with Computers categories.

The convergence of artificial intelligence (AI) and pharmaceutical sciences marks a transformative era in health care—one where data-driven insights, predictive modeling, and intelligent automation are redefining how we discover, develop, regulate, and deliver medicines. This book, AI in Pharmacy: Shaping the Future of Health Care, is a response to that paradigm shift. As a researcher and educator deeply rooted in regulatory affairs, nanomedicine, and translational pharmacology, I have witnessed firsthand the growing need for a cohesive understanding of how AI technologies can be harnessed to solve complex challenges in drug development, clinical trials, pharmacovigilance, and personalized medicine. This book is born out of that need—to bridge the gap between pharmaceutical science and computational innovation. The chapters within explore the multifaceted applications of AI across the pharmaceutical value chain. From machine learning algorithms that accelerate drug discovery to neural networks that optimize dosage regimens, and from AI-powered regulatory compliance tools to intelligent systems for adverse event detection, each section is designed to illuminate the potential and limitations of these technologies. Special attention is given to ethical considerations, data integrity, and the evolving regulatory landscape that governs AI integration in health care. This book is intended for a diverse audience: students seeking to understand the future of pharmacy, researchers aiming to incorporate AI into their experimental workflows, regulatory professionals navigating digital transformation, and clinicians curious about the implications of intelligent therapeutics. It is both a primer and a provocation—inviting readers to imagine, question, and contribute to the future we are collectively shaping. I extend my gratitude to the mentors, collaborators, students & my family members mother, brother, my son who have inspired this work, and to the global scientific community whose interdisciplinary efforts continue to push the boundaries of possibility. May this book serve as a catalyst for innovation, dialogue, and responsible advancement in the age of intelligent health care.

Bioinformatics And Beyond

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Author : Moolchand Sharma
language : en
Publisher: CRC Press
Release Date : 2025-03-19

Bioinformatics And Beyond written by Moolchand Sharma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-19 with Technology & Engineering categories.

This book is a comprehensive exploration of the dynamic interplay between bioinformatics and artificial intelligence (AI) within the healthcare landscape. This book introduces readers to the foundational principles of bioinformatics and AI, elucidating their integration and collaborative potential. Bioinformatics and Beyond: AI Applications in Healthcare explores the transformative impact of data-driven insights, showcasing the applications of machine learning in diagnostics, personalized medicine, and genomic advancements. The book unveils the pivotal role AI plays in accelerating pharmaceutical research. Moreover, it addresses the practical implementation of AI in clinical decision support systems, while also critically examining challenges and ethical considerations associated with these technologies. Finally, the book looks toward the future, envisioning emerging trends and technologies that promise to reshape the future of healthcare. Aimed at professionals, researchers, and students across diverse disciplines, this book serves as an invaluable guide to understanding and navigating the evolving landscape of AI applications in healthcare. This book is tailored to meet the needs of scientists, researchers, practitioners, professionals, and educators actively engaged in the realms of bioinformatics, artificial intelligence, and healthcare. It will be an indispensable resource for those seeking advanced strategies to address challenges and harness opportunities in the rapidly evolving fields of medical and biomedical research.

Artificial Intelligence And Machine Learning In Drug Design And Development

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