Reliable and Robust AI in Pharmaceutical R&D

This title addresses a critical gap in the current AI-for-pharma literature: the movement from algorithmic proof-of-concepts to reliable, validatedand governable AI-supported systems that can be used in regulated pharmaceutical research and development. Rather than treating model performance as sufficient evidence of value, the book examines how data, models, workflows, human review, vendor systems and lifecycle controls must be connected into reviewable and defensible evidence records. The volume covers data taxonomy and governance, benchmarking and reproducibility, model risk management, validation across discovery and translational research, generative models, large language models, closed-loop discovery, human oversight, regulatory-ready evidence, organisational governance, vendor due diligence, maturity assessment, case studies and future regulatory convergence. Designed as a long-life reference for scientists, quality and regulatory professionals, technology developers, vendors, managers and decision-makers, the book provides practical assurance concepts, decision frameworks and documentation principles for the responsible adoption of AI in pharmaceutical R&D and adjacent GxP environments. Key FeaturesPresents an assurance-first framework for reliable and robust artificial intelligence in pharmaceutical research and development. Connects data governance, semantic integrity, benchmarking, reproducibility and model risk management to decision-grade evidence. Covers generative models, large language models, retrieval-augmented generation, closed-loop discovery and laboratory automation through the lens of validation, provenance and control. Explains why validation follows the decision, not the algorithm, and why regulatory-ready artificial intelligence is an evidence state rather than a model category. Addresses organisational governance, human oversight, procurement, vendor due diligence and local accountability for artificial intelligence adoption. Provides a long-life reference for pharmaceutical scientists, quality and regulatory professionals, AI governance teams and R&D leaders working across the medicines lifecycle.