Commercialisation Strategies and Market Penetration of Computationally Discovered Drugs: A Management Perspective on the Indian Pharmaceutical Industry

Abstract

The integration of computational drug discovery and development (CDDD) methodologies encompassing artificial intelligence, machine learning, molecular docking, virtual screening, and in silico ADMET prediction into pharmaceutical research pipelines has fundamentally altered the economics, timelines, and commercialisation dynamics of new drug development. For Indian pharmaceutical firms, which have historically dominated generic manufacturing and contract research, computational drug discovery represents both a transformational opportunity to transition toward innovation-led business models and a significant managerial challenge in terms of investment justification, technology capability building, and market access strategy. This study examines the commercialisation strategies and market penetration approaches adopted by Indian pharmaceutical firms engaged in computationally driven drug discovery, drawing on secondary data from 30 listed pharmaceutical companies and primary survey data from 55 senior R&D managers, business development executives, and strategy heads. Findings reveal that licensing-out and co-development partnerships are the dominant commercialisation models for computational drug candidates, while firms that integrate business development capabilities early in the discovery pipeline achieve significantly faster time-to-deal and higher deal valuations. Regression analysis confirms that commercialisation strategy quality is a significant predictor of R&D return on investment (beta = 0.57, p<0.001). The study proposes a Computational Drug Commercialisation Management Framework (CDCMF) to guide strategic decision-making by pharmaceutical managers, investors, and technology transfer professionals.