3DProtDTA a new deep learning model for drug-target affinity prediction

Introduction

- Accurate prediction of drug-target binding affinity (DTA) is critical in drug discovery to filter out ineffective drug candidates early and reduce failures in clinical trials

- Computational DTA techniques aim to strike a balance between accuracy and efficiency compared to detailed simulations

- Recent advances use deep learning and graph representations to better capture molecule connectivity

Components

- Represents proteins as residue-level graphs with amino acid nodes, covalent and non-covalent edge features

- Uses AlphaFold structures to enable modeling of graph representations of proteins

- Ligands represented as molecular graphs plus morgan fingerprints to combine complementary information

- Graph neural networks (GNNs) employed to encode both protein and ligand graphs, with fully connected layers and graph pooling to enable end-to-end learning for affinity prediction

- Fully connected layers combined with graph pooling used for affinity prediction

- It represents both proteins and ligands (drug candidates) as graphs, retaining more information about their connectivity and 3D arrangements compared to linear representations like sequences or SMILES strings

- For proteins, it generates residue-level graphs with nodes representing amino acids and edges encoding covalent and non-covalent interactions i.e features capture sequence, structure, and binding properties

Methods

- Benchmarked on Davis and KIBA datasets against state-of-the-art methods

- Compared multiple GNN architectures and graph pooling techniques

- Used grid search and cross-validation to select optimal model architecture

- Trained end-to-end model on interaction datasets to predict binding affinities

Key Findings

- Outperforms previous methods on all evaluation metrics on both benchmarks

- Particularly significant gains on Davis set over next best method

- Ablation studies indicate combined ligand features and graph pooling choices help most

- Specific GNN architecture less crucial than representations and end-to-end integration

- Analysis of different components indicates combined ligand features and graph pooling choices contribute most to performance gains

Limitations

- Relies on AlphaFold structures - biases possible compared to experimental structures

- Residue-level graphs simplify protein complexity compared to atomic-level encodings

-The GNN architecture itself is less impactful

Future Prospects

- Integrate experimental structures where available to complement AlphaFold

- Construct atomic-level protein graphs

- Encode more physics-based descriptors e.g. flexibility, energies

Conclusion

- 3DProtDTA advances state-of-the-art in DTA prediction through integrated, end-to-end learning

- Results highlight value of graph representations and importance of modelling connectivity

- Provides strong baseline with room for improvement as more structured data becomes available

In conclusion, the use of AlphaFold structures, graph representations, and end-to-end learning appear to enable superior modelling of binding affinities

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