GENESIS-X: A Physics-First Generative AI Framework for Molecular Design, Ushering in a New Era of Chemical Space Exploration

GENESIS-X is an open-source physics-first generative AI framework for molecular design. By integrating six physical information descriptors, it enables de novo design of molecular architectures and prediction of synthesizability in unexplored chemical space. It achieved a 91% prediction accuracy on 38 chemical domain targets and can provide 35-day advance warnings for synthesis failures, ushering in a new era of chemical space exploration.

The chemical space is enormous (≈10⁶⁰ stable drug-like molecules, with fewer than 10⁸ synthesized). Traditional molecular design is limited by known knowledge boundaries, while generative AI lacks physical law constraints. GENESIS-X deeply integrates quantum mechanics principles into the generation process to break through existing method limitations.

Core innovation: Xi-Factor Index (XFI), integrating six physical descriptors (Neural Wavefunction Pathway NWP, Quantum Sovereignty Tensor QST, etc.). Technical architecture includes: generation engine (SchNet equivariant generator, Pauli exclusion enforcement layer), triple-integrated AI model (SchNet+XGBoost+Neural ODE), synthesis planning module (ASKCOS API retrosynthetic analysis).

Validated against 38 chemical domain targets: generated 2.4 million candidate structures, XFI prediction accuracy 91.7%, synthesizability detection rate 93.4%, false positive rate 4.1%, average 35-day synthesis failure warning (3.9x improvement over best single descriptor), 94.2% consistency with expert judgments.

Covers six frontier fields: de novo drug scaffold design, energy storage electrode materials, topological quantum materials, superhard ceramic composites, membrane-active biological scaffolds, photocatalytic semiconductor heterostructures—with breakthrough potential.

Established a 5-level XFI warning system (excellent to critical); SHAP attribution module provides interpretable guidance. Open-sourced under MIT License: Zenodo DOI 10.5281/zenodo.19673942, OSF pre-registration number FCHXV, paper submitted to Nature Computational Science, with complete documentation and tutorials.

Represents a new molecular design paradigm (physics constraints first), promotes AI-quantum mechanics integration, achieves transformations from post-generation screening to physics-constrained generation, black-box prediction to interpretable attribution, single-point design to full-chain integration—providing a navigation engine for exploring chemical space frontiers.