The mixing of synthetic intelligence (AI) into drug discovery has advanced from early computer-aided design to superior AI-driven methodologies, laying the muse for a transformative paradigm: Silico-driven Drug Discovery (SDD). Not like typical approaches the place AI helps remoted levels, SDD treats the whole analysis course of, together with literature understanding, speculation era, molecular design, and experimental validation, as a unified, doubtlessly autonomous system. This evaluation proposes the THINK–BUILD–OPERATE (TBO) structure as a common framework for implementing SDD and descriptions its six-level automation pathway from human-led to totally autonomous discovery. We spotlight nanomedicine as an optimum frontier for SDD resulting from its well-defined theoretical foundations, considerable multi-omics and pharmacological information, and supportive regulatory shifts. By integrating domain-specific toolchains, large-scale AI fashions, and orchestrated self-driving laboratories, SDD can speed up complicated, multidisciplinary analysis whereas lowering prices and timelines. We additional establish key challenges together with AI mannequin reliability, infrastructure interoperability, and automatic laboratory versatility that have to be addressed to attain this imaginative and prescient. In the end, the convergence of AI, superior laboratory automation, and world analysis networking holds the potential to rework drug discovery into an industrial-scale, programmable scientific enterprise.
