AI and Machine Learning in Radiopharmaceutical Development | "From Design to Therapy: The Impact of AI in Radiopharmaceutical Development"

Khushboo Gupta (u. a.)

ISBN 10: 6208418275 / ISBN 13: 9786208418274

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

AI and Machine Learning in Radiopharmaceutical Development | "From Design to Therapy: The Impact of AI in Radiopharmaceutical Development" | Khushboo Gupta (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208418274 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 131245447

Bibliographic Details

• Title: AI and Machine Learning in Radiopharmaceutical Development | "From Design to Therapy: The Impact of AI in Radiopharmaceutical Development"
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6208418275
• ISBN 13: 9786208418274
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

AI and machine learning (ML) are increasingly transforming the field of radiopharmaceutical development, offering new ways to design, optimize, and evaluate these critical compounds. Radiopharmaceuticals, which are used for both diagnostic imaging and targeted therapy, require precise formulation and testing to ensure safety and efficacy. AI and ML technologies can accelerate this process by analyzing vast amounts of complex data, identifying patterns, and making predictions that were previously time-consuming or difficult.Machine learning algorithms, such as deep learning and reinforcement learning, are used to model molecular interactions, predict the behavior of radiopharmaceuticals within the body, and optimize the selection of radioisotopes for specific targeting. AI-driven tools also enable faster drug discovery by simulating and predicting the outcomes of radiopharmaceutical interactions with biological systems, thus reducing the need for extensive trial and error.