Converging Pharmacy Science and Engineering in Computational Drug Discovery

Converging Pharmacy Science and Engineering in Computational Drug Discovery

Release Date: April, 2024|Copyright: © 2024 |Pages: 322
DOI: 10.4018/979-8-3693-2897-2
ISBN13: 9798369328972|ISBN13 Softcover: 9798369366349|EISBN13: 9798369328989
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Description & Coverage
Description:

The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery.

Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence in Drug Discovery
  • Big Data Analytics
  • Biopharmaceutical Innovations and Delivery Systems
  • Case Studies in Computational Drug Discovery
  • Computational Chemistry for Drug Optimization
  • Data Management
  • Drug Repurposing
  • Engineering Approaches in Pharma Research
  • Ethical and Legal Considerations
  • High-Throughput Screening Techniques
  • Industry Perspectives and Success Stories
  • Introduction to Computational Drug Discovery
  • Molecular Modeling and Simulation
  • Multidisciplinary Collaboration in Drug Discovery
  • Pharmacodynamics
  • Pharmacokinetics
  • Virtual Screening
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Editor/Author Biographies
Rati Kailash Prasad Tripathi is currently working as an Assistant Professor at Assam Central University, Silchar, Assam, India. She received her M. Pharm. (2011) and Ph.D. (2016) degrees from the Indian Institute of Technology, Banaras Hindu University, Varanasi (India); where she specialized in Pharmaceutical Chemistry with peculiar interest in synthetic medicinal chemistry, computational chemistry and molecular modelling. She has to her credit many scientific publications in journals of international repute. She has also authored 02 books and several book chapters. She is also a recipient of several research grants and prestigious awards like Senior Research Fellowship from the Indian Council of Medical Research, Government of India and Shri C. Subramaniam Award for Excellence in Character from Bhartiya Vidya Bhavan, Mumbai. She is an active member of many scientific Indian associations viz. Indian Pharmacy Graduates’ Association (IPGA) and The Association of Pharmaceutical Teachers of India (APTI). She has also mentored several graduate, post graduate and PhD students. She also serves as an editorial board member and reviewer for many international journals of repute.
Shrikant Tiwari (Senior Member, IEEE) was born in Karuwa Village, Maihar, Madhya Pradesh, India in 15th Aug. He received his Ph.D. in the Department of Computer Science & Engineering (CSE) from the Indian Institute of Technology (Banaras Hindu University), Varanasi (India) in 2012 and M. Tech. in Computer Science and Technology from University of Mysore (India) in 2009. Currently, he is working as an Associate Professor in the Department of Computer Science & Engineering (CSE) School of Computing Science and Engineering (SCSE) at Galgotias University, Greater Noida, Uttar Pradesh (India). He has authored or co-authored more than 35 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His current research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics. Dr. Tiwari is a member of ACM, IET, FIETE, CSI, ISTE, IAENG, SCIEI. He is also a guest editorial board member and a reviewer for many international journals of repute.
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