https://github.com/deeprank/ponder

PointNet for Deep Rank: protein-protein interaction scoring using neural networks
https://github.com/deeprank/ponder

deep-learning pdb point-cloud pointnet protein-protein-interaction structural-bioinformatics

Last synced: 24 days ago
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PointNet for Deep Rank: protein-protein interaction scoring using neural networks

• Host: GitHub
• URL: https://github.com/deeprank/ponder
• Owner: DeepRank
• License: apache-2.0
• Created: 2018-06-21T08:27:26.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-05-27T10:59:31.000Z (almost 6 years ago)
• Last Synced: 2025-10-23T13:40:48.964Z (5 months ago)
• Topics: deep-learning, pdb, point-cloud, pointnet, protein-protein-interaction, structural-bioinformatics
• Language: Python
• Homepage:
• Size: 383 KB
• Stars: 9
• Watchers: 0
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# General

Experimental deep learning architecture for scoring protein-protein interactions.

See [PointNet paper](https://arxiv.org/abs/1612.00593) for original architecture description. This implementation contains two architectures, neither of which contain the transformer networks, so can be considered variants of the *vanilla* version of PointNet. The first differs merely in its dropout rate (50%), whereas the second is a novel architecture called *Siamese PointNet*, visible in the image below.

![Siamese PointNet](/doc/SiamesePointNet_architecture.png)

Other adaptations include cosine annealing learning rate decay, which has been implemented to improve accuracy and generalizability of the trained network (see [Stochastic Gradient Descent with Warm Restarts](https://arxiv.org/abs/1608.03983)), and a custom loss function introducing a bias in learning towards higher scoring decoys.

![FavorHigh loss](/doc/FavorHighLoss.png)

# Dependencies

* Python 3.x

* [H5Py](http://www.h5py.org/) for fast data retrieval

* [PyTorch <0.4](https://github.com/pytorch/pytorch) and its dependencies

* Data conversion uses [DeepRank](https://github.com/DeepRank/deeprank) and its dependencies

* [Seaborn](https://github.com/mwaskom/seaborn) for plotting

# Usage

*python train.py*

```

  --batch_size BATCH_SIZE   Input batch size (default = 256)

  --num_points NUM_POINTS   Points per point cloud used (default = 1024)

  --num_epoch NUM_EPOCH     Number of epochs to train for (default = 15)

  --CUDA                    Train on GPU

  --out_folder OUT_FOLDER   Model output folder

  --model MODEL             Model input path

  --data_path DATA_PATH     Path to HDF5 file

  --lr LR                   Learning rate (default = 0.0001)

  --optimizer OPTIMIZER     What optimizer to use. Options: Adam, SGD, SGD_cos

  --avg_pool                Use average pooling for feature pooling (instead of default max pooling)

  --dual                    Use Siamese PointNet architecture

  --metric METRIC           Metric to be used. Options: irmsd, lrmsd, fnat, dockQ (default)

  --dropout DROPOUT         Dropout rate in last layer. When 0 replaced by batchnorm (default = 0.5)

  --root                    Apply square root on metric (for DockQ score balancing)

  --patience PATIENCE       Number of epochs to observe overfitting before early stopping

  --classification          Classification instead of regression

```

The network takes the atoms taking part in an interaction as point cloud data. Data conversion can be performed using the *extract_pc.py* script.

Data is saved in HDF5 format containing 3 groups: train, test and "holdout" data. Datasets within these groups contain atom features with *float32* precision and attributes containing the iRMSD, lRMSD, FNAT, and DockQ scores.

# Current state

* Architecture & training scripts have been fully implemented