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 peer-reviewed references from prestigious journals and international research institutions, providing a robust, evidence-based foundation for future empirical validations.本概念性论文确立了逆向化学（Inverse Chemistry）的基础原理——这是一门新兴交叉学科，通过将预期属性回溯至分子结构与组成，来解决化学科学中的逆向问题。本研究依托先进计算方法、机器学习与严谨的数学框架，勾勒出一套涵盖贝叶斯推断、精细化敏感性分析、不确定性量化与可证伪性准则的多维研究路径。该框架纳入了材料科学、药物发现与蛋白质工程领域的逆向设计策略，并辅以精准的数学推导、可复现的仿真实验与Python代码实现作为支撑。高保真TikZ可视化效果与科学严谨的图表对复杂流程进行阐释，保障了研究的科学严谨性与可复现性。本研究整合了来自顶级期刊与国际科研机构的同行评议文献中的核心观点，为后续的实证验证提供了坚实且循证的基础。