The Science Behind Alphafold
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The Science Behind AlphaFold
AlphaFold, a groundbreaking AI system, has cracked the code on protein structure prediction, a challenge that baffled scientists for decades. This book explores the science behind AlphaFold, delving into deep learning, big data, and the inner workings of this remarkable program. Uncover how AlphaFold is revolutionizing protein science, with the potential to accelerate drug discovery, personalize medicine, and design innovative materials. This comprehensive guide explores: The significance of protein structures and the challenges of prediction How AlphaFold leverages deep learning and vast data resources The process of protein structure prediction with AlphaFold, including its strengths and limitations The ethical considerations surrounding AI in protein science The exciting future applications of AlphaFold in various scientific fields Whether you're a scientist, student, or simply curious about the future of biology, this book provides a clear and engaging exploration of AlphaFold and its transformative impact on protein science.
Artificial Intelligence For Science: A Deep Learning Revolution
This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
Artificial Intelligence and Molecular Biology
These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.