Vol. 7, Issue 2, Part E (2025)

In-silico pharmaceutics employs computational models and simulations to predict the behavior of pharmaceutical formulations, thereby optimizing drug development workflows. This approach significantly reduces the time, cost, and ethical concerns associated with extensive physical experiments and animal testing. Key methodologies include molecular docking and dynamics for evaluating drug-excipient interactions, Physiologically Based Pharmacokinetic (PBPK) modeling for systemic drug behavior prediction, and Computational Fluid Dynamics (CFD) for process optimization. Furthermore, Artificial Intelligence and Machine Learning models enable rapid screening of formulation variables, while digital twins provide real-time monitoring and predictive control of manufacturing processes. The integration of these tools with quantum simulations promises enhanced accuracy and speed in molecular design. Future perspectives center on the democratization of advanced computational power via cloud-based quantum platforms and the convergence with multi-omics data, paving the way for data-driven, personalized drug development.