![]() 4,5,7 They enable the efficient extraction of molecules with significant pharmacological activity from a vast pool of candidate substances. 6 These methodologies have achieved success in fields such as drug discovery and inorganic material exploration. 1–5 One key feature of these approaches is their capacity to rapidly extract statistically significant relationships from constructed databases using data science techniques. Introduction Materials informatics and cheminformatics are scientific disciplines aiming to process and derive meaningful chemical and physical insights from correlations between the structures and properties of compounds and materials. This can partially alleviate challenges posed by the “Ugly Duckling” theorem and limited data availability. We propose using OpenAI generative pretrained transformer 4 (GPT-4) language model for explanatory variable selection, leveraging its extensive knowledge and logical reasoning capabilities to embed domain knowledge in tasks predicting structure–property correlations, such as the refractive index of polymers. Current methodologies don't entirely bypass this theorem and may lead to decreased accuracy with unfamiliar data. The “Ugly Duckling” theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. ![]()
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