India, May 1 -- AI is expected to address these structural challenges across the R&D value chain. It can reduce early-stage failures, predict biological relevance, and shorten discovery timelines, with quantifiable, industry-validated impact. The technology can cut preclinical R&D costs significantly, driven by improvements in target selection, virtual screening, and molecule property prediction. Discovery cycles are likely to become more efficient, shrinking from years to months. Across the drug lifecycle, lower costs and shorter development cycles are expected, varying by molecule complexity and degree of enterprise AI adoption. AI can also accelerate regulatory submissions by automating data compilation across internal and external sou...