Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions. However, current models typically rely on 2-dimensional molecular representations as input, and w...
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Zusammenfassung: | Neural Networks (GNNs) have revolutionized the molecular discovery to
understand patterns and identify unknown features that can aid in predicting
biophysical properties and protein-ligand interactions. However, current models
typically rely on 2-dimensional molecular representations as input, and while
utilization of 2\3- dimensional structural data has gained deserved traction in
recent years as many of these models are still limited to static graph
representations. We propose a novel approach based on the transformer model
utilizing GNNs for characterizing dynamic features of protein-ligand
interactions. Our message passing transformer pre-trains on a set of molecular
dynamic data based off of physics-based simulations to learn coordinate
construction and make binding probability and affinity predictions as a
downstream task. Through extensive testing we compare our results with the
existing models, our MDA-PLI model was able to outperform the molecular
interaction prediction models with an RMSE of 1.2958. The geometric encodings
enabled by our transformer architecture and the addition of time series data
add a new dimensionality to this form of research. |
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DOI: | 10.48550/arxiv.2212.09991 |