Founding the Science of Inverse Chemistry: A Conceptual Framework for Property-to-Structure Mapping in Chemical Sciences

This conceptual paper establishes the foundational principles of Inverse Chemistry, a novel interdisciplinary field that addresses the inverse problems in chemical sciences by mapping desired properties back to molecular structures and compositions. Drawing upon advanced computational methodologies, machine learning, and rigorous mathematical frameworks, we delineate a multi-dimensional approach encompassing Bayesian inference, detailed sensitivity analysis, uncertainty quantification, and falsifiability criteria. The framework incorporates inverse design strategies in materials science, drug discovery, and protein engineering, supported by precise mathematical derivations, reproducible simulations, and Python code implementations. High-fidelity TikZ visualizations and scientifically accurate graphs illustrate complex processes, ensuring scientific rigor and reproducibility. This work integrates insights from 97 peer-reviewed references from prestigious journals and international research institutions, providing a robust, evidence-based foundation for future empirical validations.