What question did this study set out to answer?

This study aims to improve the prediction of drug-target binding affinities using a novel neural network architecture.

May 7, 2026Journal of Chemical Theory and Computation

MolXProt: A Cross-Attention Transformer-Based Graph Neural Network for Protein-Ligand Binding Affinity Prediction

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Authors

BCBruno CuccoUrology Team

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Implication

Randomized trial demonstrates a novel model for predicting binding affinities in protein-ligand interactions, indicating unique insights into binding mechanisms.

Key Points

This study aims to improve the prediction of drug-target binding affinities using a novel neural network architecture.
Introduced the MolXProt architecture using a graph neural network and transformer-based cross-attention.
Integrated graph ligand representations with protein language models to analyze over 100000 protein-ligand pairs.
Conducted calibration bias analysis and latent space analysis to understand model performance and biases.
Achieved 50% of predictions within ±1.0 kcal/mol and 80% within ±2.0 kcal/mol.
Demonstrated the model learns key binding pocket residues while under-representing hydrogen-bonding networks.
Posthoc isotonic correction partially mitigated overprediction of strong binders and underprediction of weak binders.

Cite This Study

Bruno Cucco (2026) studied this question.

synapsesocial.com/papers/69fc2c1f8b49bacb8b347b64 https://doi.org/10.1021/acs.jctc.6c00026

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Implication

Randomized trial demonstrates a novel model for predicting binding affinities in protein-ligand interactions, indicating unique insights into binding mechanisms.

MolXProt: A Cross-Attention Transformer-Based Graph Neural Network for Protein-Ligand Binding Affinity Prediction

Discussion

Member takes

Implication

Key Points

Cite This Study

MolXProt: A Cross-Attention Transformer-Based Graph Neural Network for Protein-Ligand Binding Affinity Prediction

Discussion

Member takes

Implication

Key Points

Cite This Study

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