A Unified Computational Framework for the Integration of AI Models in Structure-Based Drug Design

The rapid proliferation of sophisticated computational models for drug discovery has created unprecedented opportunities for innovation, yet the field lacks comprehensive frameworks to systematically integrate these diverse tools into coherent workflows. Although these models demonstrate remarkable individual capabilities, researchers are forced to navigate fragmented toolsets requiring extensive computational expertise, limiting the practical impact of these advances and creating an accessibility problem. In this paper, we present a comprehensive and modular computational drug discovery pipeline that provides the first systematic framework for integrating diverse state-of-the-art models into an accessible unified drug discovery workflow. The workflow is based on the integration of state-of-the-art generative and docking models, with a special focus on ensuring the synthetic accessibility and real world scenario plausibility of the proposed molecules.