Comprehensive Overview of “Artificial Intelligence in Drug Development and Drug Discovery

Abstract

Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 billion by 2030–2034, supported by massive investments exceeding $60 billion in the last decade. Today, nearly all major pharmaceutical companies are integrating AI into their R&D pipelines. Applications span target identification, drug design, protein structure prediction, virtual screening, ADMET profiling, and clinical trial optimization. Break throughs such as AlphaFold for protein structures, generative design for molecules, and AI-enhanced clinical trials have already shown measurable impact—cutting recruitment times and allowing adaptive protocols. Real-world successes, including Insilico Medicine’s INS018-055 and Exscientia’s oncology candidates, highlight how AI-driven drugs are progressing faster into clinical testing with higher success potential. Regulators like the FDA are also advancing new frameworks to ensure transparency and reliability in AI applications. While challenges remain—such as biased datasets, model interpretability, and integration into established workflows—emerging technologies like federated learning, multimodal AI, and autonomous labs signal even greater advances ahead. Ultimately, AI is steering drug discovery away from trial-and-error toward predictive, data-driven development, unlocking safer and more effective therapies.