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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: Jun 28, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Evaluating Molecular Representations for Predicting Cyclodextrin-PFAS Binding Energy with Machine Learning: Domain

Cole Brzakala¹, Othonas A Moultos², Jan Peter van der Hoek^1,3

¹Water Management Department, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1 2628CN Delft, Netherlands.

Journal of Chemical Information and Modeling

|June 26, 2026

Summary

This summary is machine-generated.

Machine learning models show promise for predicting cyclodextrin-based polymer (CDP) interactions with per- and polyfluoroalkyl substances (PFAS). However, domain shift challenges limit generalizability for designing effective PFAS removal solutions.

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Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach

Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach

Published on: June 23, 2026

Area of Science:

Environmental Chemistry
Computational Chemistry
Materials Science

Background:

Per- and polyfluoroalkyl substances (PFAS) are persistent pollutants resistant to conventional water treatment.
Cyclodextrin-based polymers (CDPs) offer a sustainable alternative for PFAS adsorption via host-guest complexation.
Understanding and quantifying cyclodextrin-PFAS binding interactions is crucial for optimizing CDP design.

Purpose of the Study:

To evaluate machine learning (ML) approaches for modeling host-guest interactions between cyclodextrins (CDs) and PFAS.
To compare various molecular representations and ML architectures for predicting CD-PFAS binding energies.
To assess the generalizability of ML models and the impact of transfer learning and finetuning.

Main Methods:

Generated molecular embeddings for experimental host-guest pairs using representations like Mordred, ECFP, ChemBERTa, and UniMol2.
Compared embeddings using AlignedUMAP visualizations and nearest neighbor analyses.
Trained and evaluated ML models on the OpenCycloDB dataset, incorporating transfer learning and finetuning, and tested on external CD-PFAS datasets.

Main Results:

All molecular embeddings captured relevant chemical features, with UniMol2 showing distinct embedding space characteristics.
ML model performance varied by embedding and architecture, achieving moderate accuracy on the primary dataset.
Pretraining embeddings and finetuning ChemBERTa embeddings improved predictive performance.
External validation revealed limited generalizability due to domain shift challenges between general CD data and specific CD-PFAS applications.

Conclusions:

Molecular representation is critical for accurate host-guest binding prediction in small-data scenarios.
Domain shift presents a significant challenge for applying ML models to specialized CDP-PFAS applications.
Transfer learning and finetuning show potential for addressing domain shift in data-driven CDP design for sustainable PFAS removal.