DockMaster-Songdo A Three-Level Quantum Adoption Framework for Protein Preprocessing
Sungil Oh
DockMaster-Songdo™ is a web-based AI and quantum-inspired protein preprocessing framework designed to automate and standardize critical preparation workflows for structure-based drug discovery. The platform integrates rule-based automation, LLM-driven workflows, and a future-ready three-level quantum adoption architecture for protein and small-molecule analysis. This work introduces a scalable framework consisting of: Level 0: Classical baseline preprocessing Level 1: Bio-physics-inspired enhancement Level 2: Quantum-optional hybrid architecture The platform automates essential preprocessing tasks including water removal, hydrogen addition, charge assignment, ligand and metal handling, validation, and PDBQT export. In addition, the framework maps practical near-term quantum algorithms such as QAOA, fragment-VQE, quantum-kernel SVM, and FMO/VQE into real-world molecular preprocessing workflows. DockMaster-Songdo™ aims to provide a reproducible, transparent, and regulatory-aware preprocessing environment aligned with international drug discovery standards including FDA, EMA, PMDA, NMPA, and K-FDA expectations. The architecture is designed to operate even without quantum hardware availability, enabling graceful transition from classical AI pipelines to future hybrid quantum-classical systems. This research represents an independent effort toward practical Quantum-AI adoption in computational biology, automated protein preprocessing, and next-generation drug discovery infrastructure.
•Feb 27, 2026•0 citations