Deep learning a transferable model for drug-receptor binding-energy
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
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Sammanfattning
Machine learning has enabled numerous advances in computational chemistry which
have previously been out of reach due to the complex forces that govern atomic in teractions. In particular, the construction of machine learning-based force fields for
describing atomic interactions has been an attractive area of research because it
may allow to close gaps between the efficiency of molecular dynamics simulations
and accuracy of theoretical models. In this thesis, we explore the topic of predicting
ligand-protein interaction energy using a deep learning, energy-based model that
is able to capture molecular interactions based on an input 3D molecular graph.
Building upon graph neural networks, our model can predict interaction energies by
obtaining the underlying force field that governs the interaction between a protein
and a drug-like molecule. We used protein-ligand interaction data from curated
databases to optimize an energy function for our model. We adopt the score match ing approach to construct two loss functions and compare their performance. We
show that in combination with an energy model, the use of score matching allows us
to avoid estimating a partition function in our models, which is important for our
large systems and leads to a better learning objective. We discuss training strate gies, and provide two metrics for evaluating the performance of the model. While we
are able to successfully model atomic interactions to some extent, we found a major
challenge in handling the memory requirements for representing protein-ligand data
that substantially limited our ability to explore the configuration and hyperparam eter space in our models. As such, we complement our protein-ligand studies by
also training on a artificial but simplified dataset, which allowed us to explore the
hyperparameter space more in depth. We conclude by discussing possible strategies
for the future development of these models.
Beskrivning
Ämne/nyckelord
Computer science, deep-learning, energy-based models, drug discovery, protein, ligand, binding energy