Deependra Singh · 2026 · Journal of Pharmaceutical Technology, Research and Management
Paper
Background: The pharmaceutical industry is undergoing rapid digital transformation, generating vast and complex datasets that challenge traditional drug discovery workflows. Artificial intelligence (AI) has emerged as a powerful solution capable of processing large-scale clinical, biological, and chemical information with high precision. Its ability to learn from data, uncover hidden patterns, and automate complex tasks positions AI as a transformative force in modern drug development. Objective: This editorial, through an AI lens in drug discovery, demonstrates the significance of AI applications in target identification, hit generation, lead optimization, predictive toxicology, ADMET profiling, and clinical trial design, and the issues and ethical considerations. The difficulties were acknowledged. Results: AI showed massive power in foreseeing drug–target interactions, virtually testing millions of compounds, and creating new chemical structures with better pharmacological profiles. Deep learning techniques were far superior to conventional machine learning methods when predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. The use of AI to perform virtual screening and generate modeling rapidly led to the identification of new drug candidates, while clinical trial design was improved through data-driven algorithms enabling enhanced patient stratification and adaptive protocol utilization. Moreover, AI techniques allowed for the earliest possible toxicity prediction, thus lowering last-stage failures and overall development costs. Conclusion: AI is a significant paradigm shift in drug discovery, which means therapeutics will be developed in a shorter period, at a lower cost, and with higher accuracy. However, the maximum benefit of AI can be achieved only if the technical, ethical, and regulatory challenges are solved through collaborative, transparent, and safe usage of AI-driven innovation facilitation frameworks.
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