This thesis discusses the impact of digital technologies, specifically artificial intelligence (AI), on drug discovery in India, especially the target identification phase. AlphaFold, developed by DeepMind, is mentioned as one of the greatest innovations in protein structure prediction and its significance is compared to those of other AI-based applications including IBM Watson, Atomwise, and BenevolentAI. The study is positivist, quantitative in nature and based on an online survey administered to students, early-career researchers, and industry professionals in India. Responses of 129 participants gave insights on awareness, adoption, and benefits as well as challenges of using AI in drug discovery. Data were analyzed with descriptive statistics and visual comparison of students and professionals. The results indicate that students possess more theoretical knowledge about the AI tools like AlphaFold, whereas professionals reveal more variated but restricted practical use, with most of them relying on in-house tools. The benefits noted by the two groups were that of speed in target identification, better accuracy and lower costs. Nonetheless, low skills, prohibitive costs, data availability, and regulatory uncertainty remain some of the challenges that limit the wider adoption in India. The research finds that, although the pharmaceutical industry in India has started to experiment with AI in research, its wide-scale integration has been low. It recommends that more investments in AI training, improvement of data infrastructure, and friendly regulations should be introduced to encourage adoption. In academia, the gap between awareness and practice can be filled by integrating AI tools into the curricula. On the whole, this dissertation helps us to realize how AI can help speed up the pace of earlystage drug discovery in India and provides viable suggestions to researchers, industry professionals, and policymakers to enhance digital transformation in the pharmaceutical sector.