Artificial Intelligence in Drug Design

"AI in Drug Design" is a book that explores the intersection of artificial intelligence (AI) and pharmaceutical research. Written by experts in the field, it highlights the innovative ways in which AI is revolutionizing the process of drug discovery and design.The book delves into various AI techniques and algorithms employed in drug design, such as machine learning, deep learning, and data mining. It showcases how these techniques are used to analyze vast amounts of biological and chemical data, accelerating the identification and optimization of potential drug candidates. Readers will gain insights into the applications of AI in target identification, virtual screening, molecular docking, and de novo drug design. The authors discuss how AI algorithms can predict the activity and toxicity of drug compounds, enabling researchers to prioritize promising candidates and reduce the time and cost associated with traditional trial-and-error approaches.

Autorentext

Tushar Hrishikesh Jaware est doyen de la recherche et du développement à l'Institut de technologie R C Patel de Shirpur. Il a plus de 18 ans d'expérience universitaire. Il est directeur de thèse en ingénierie E&TC à la North Maharashtra University et à la DBATU Lonere. Il a récemment reçu le Loksatta Tarun Tejankit Puraskar.

Klappentext

"AI in Drug Design" is a book that explores the intersection of artificial intelligence (AI) and pharmaceutical research. Written by experts in the field, it highlights the innovative ways in which AI is revolutionizing the process of drug discovery and design.The book delves into various AI techniques and algorithms employed in drug design, such as machine learning, deep learning, and data mining. It showcases how these techniques are used to analyze vast amounts of biological and chemical data, accelerating the identification and optimization of potential drug candidates. Readers will gain insights into the applications of AI in target identification, virtual screening, molecular docking, and de novo drug design. The authors discuss how AI algorithms can predict the activity and toxicity of drug compounds, enabling researchers to prioritize promising candidates and reduce the time and cost associated with traditional trial-and-error approaches.