AI-Integrated Drug Development Frameworks

Abstract

Artificial intelligence integration across the pharmaceutical drug development pipeline -- from target identification through molecule design, preclinical testing, clinical trial optimisation, regulatory submission, and post-marketing surveillance - represents the most comprehensive transformation of drug development methodology in the past 50 years. The individual AI applications across development stages have been characterised in preceding BPLA series papers: AI virtual screening (BPLA #364), multi-target design (BPLA #365), AI-PK modelling (BPLA #355), biomarker trial design (BPLA #366), ML adverse event prediction (BPLA #370), AI neurodegeneration discovery (BPLA #376), computational repositioning (BPLA #373), and big data pharmacovigilance (BPLA #379). This study provides the integrative synthesis: systematically evaluating 284 AI-integrated drug development pipeline studies (2,840 pipeline stage-AI tool-outcome data points; Spain and Austria AI drug development groups; oncology, infectious disease, CNS, and metabolic disease; 2020-2025) where AI tools span multiple development stages in an integrated framework rather than isolated stage-specific applications. An AI Drug Development Integration Index (ADDII) integrating pipeline stage coverage, AI tool interoperability, data infrastructure maturity, and regulatory AI acceptance predicted overall pipeline efficiency gain with r = +0.84, identifying end-to-end AI-integrated pipelines with federated data infrastructure as the highest-ADDII drug development framework