Code supporting the publication: Evaluating Molecular Representations for Predicting Cyclodextrin-PFAS Binding Energy with Machine Learning: Domain Transfer and Data Limitations

This repository contains the data and analysis code for evaluating molecular representations for predicting binding between cyclodextrins and PFAS molecules via machine learning. The project aims to explore CD-PFAS binding prediction, impacts of limited data, and most effective molecular representations for later cyclodextrin-based polymer (CDP) modeling. These models will then be used for novel CDP design for PFAS removal from water systems. Data used in this analysis comes from the OpenCycloDatabase, a consolidation of experimental binding results from published studies for cyclodextrin and guest molecule pairs, and two additional experimental studies, containing experimental binding data for cyclodextrins and PFAS molecules.