Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

Overview

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

Requirements

  • python 0.10+
  • rdkit 2020.03.3.0
  • biopython 1.78
  • openbabel 2.4.1
  • numpy 1.19.2
  • scipy 1.5.2
  • torchvision 0.7.0

Conda enviroment is highly recommended for this implementation

Data Preparation for classification models

Data preperation requires the ligand and protein to be in a mol format readable by rdkit .mol, .mol2, and .pdb are readily handled by rdkit .sdf is easily handled with openbabel conversion, made convenient with the pybel wrapper

Both files can then be fed into extractM2.py where the cropping window can be adjusted on line 29 The extract method will operates best if the initial protein file is in pdbqt format. For easy model integration it is best to store the m2 protein window produced by the extract script along with the original protein ex: pickle.dump((m1,m2), file)

Once cropped complexes are stored, their numpy featurization files can be created. Files for the different models are labeled in the Data_Prep directory

The scripts are designed to use keys that reference the cropped and stored pairs from the previous step. Users will need to alter scripts to include their desired directories, as well as key traversal. Once these changes have been made, the scripts can be called with

python -W ignore gnn[f/p]_data_prep.py

Data Preparation for regression models

The data needs to be in mol format as similar to classification models. We have provided some sample mol files representing protein and ligand. Here the protein is cropped at 8Å window using the extract script as mentioned previously.

The cropped protein-ligand can be used to create features in numpy format. Sample training and test keys along with the corresponding pIC50 and experimental-binding-affinity (EBA) labels are provided in keys folder. All the files are saved in pickle format with train and test keys as list and the label files as disctionary with key corresponding to the train/test key and value corresponding to the label. The prepare_eba_data.py and prepapre_pic50_data.py uses the cropped protein-ligand mol files to create the correspnding features for the model and save them in compressed numpy file format in the corresponding numpy directory.

These scripts can be called as:

python repare_pic50_data.py <path to pkl-mol directory> <path to save numpy features>
python repare_eba_data.py <path to pkl-mol directory> <path to save numpy features>

Training

Below is an example of the training command. Additional options can be added to the argument parser here (learning rate, layer amount and dimension, etc). Defaults are in place for undeclared parameters including a save directory.

Classfication models

python -W ignore -u train.py --dropout_rate=0.3 --epoch=500 --ngpu=1 --batch_size=32 --num_workers=0  --train_keys=<your_training_keys.pkl>  --test_keys=<your_test_keys.pkl>

Regression models

python -W ignore -u train.py --dropout_rate=0.3 --epoch=500 --ngpu=1 --batch_size=1 --num_workers=0 --data_dir=<path to feature-numpy folder> --train_keys=<your_training_keys.pkl>  --test_keys=<your_test_keys.pkl>

The save directory stores each epoch as a .pt allowing the best model inatance to be loaded later on Training and test metrics such as loss and ROC are stored in the same directory for each GPU used. Ex 3 GPUS: log-rank1.csv, log-rank2.csv, and log-rank3.csv

Owner
Neeraj Kumar
Computational Biology/Chemistry and Bioinformatics.
Neeraj Kumar
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge

Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge This is an implementation of the paper,

Mutian He 19 Oct 14, 2022
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset

CPPE - 5 CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization

Rishit Dagli 53 Dec 17, 2022
Özlem Taşkın 0 Feb 23, 2022
Predicting a person's gender based on their weight and height

Logistic Regression Advanced Case Study Gender Classification: Predicting a person's gender based on their weight and height 1. Introduction We turn o

1 Feb 01, 2022
Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.

The Face Synthetics dataset Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels. It was introduced in ou

Microsoft 608 Jan 02, 2023
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

EDM-subgenre-classifier This repository contains the code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Fea

11 Dec 20, 2022
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras. Implementation of various Deep Image Segmentation mo

Divam Gupta 2.6k Jan 05, 2023
Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs Check out the paper on arXiv: https://arxiv.org/abs/2103.13744 This repo cont

Christian Reiser 373 Dec 20, 2022
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

How to eat TensorFlow2 in 30 days ? 🔥 🔥 Click here for Chinese Version(中文版) 《10天吃掉那只pyspark》 🚀 github项目地址: https://github.com/lyhue1991/eat_pyspark

lyhue1991 9.7k Jan 01, 2023
The ARCA23K baseline system

ARCA23K Baseline System This is the source code for the baseline system associated with the ARCA23K dataset. Details about ARCA23K and the baseline sy

4 Jul 02, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
State of the art Semantic Sentence Embeddings

Contrastive Tension State of the art Semantic Sentence Embeddings Published Paper · Huggingface Models · Report Bug Overview This is the official code

Fredrik Carlsson 88 Dec 30, 2022
You Only 👀 One Sequence

You Only 👀 One Sequence TL;DR: We study the transferability of the vanilla ViT pre-trained on mid-sized ImageNet-1k to the more challenging COCO obje

Hust Visual Learning Team 666 Jan 03, 2023
Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

On the Generative Utility of Cyclic Conditionals This repository is the official implementation of "On the Generative Utility of Cyclic Conditionals"

Chang Liu 44 Nov 16, 2022
Non-Attentive-Tacotron - This is Pytorch Implementation of Google's Non-attentive Tacotron.

Non-attentive Tacotron - PyTorch Implementation This is Pytorch Implementation of Google's Non-attentive Tacotron, text-to-speech system. There is som

Jounghee Kim 46 Dec 19, 2022
Pytorch implementation for RelTransformer

RelTransformer Our Architecture This is a Pytorch implementation for RelTransformer The implementation for Evaluating on VG200 can be found here Requi

Vision CAIR Research Group, KAUST 21 Nov 22, 2022
Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)

Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT

Emirhan BULUT 102 Nov 18, 2022
A booklet on machine learning systems design with exercises

Machine Learning Systems Design Read this booklet here. This booklet covers four main steps of designing a machine learning system: Project setup Data

Chip Huyen 7.6k Jan 08, 2023
Human4D Dataset tools for processing and visualization

HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media HUMAN4D constitutes a large and multimodal 4D dataset that contains a variet

tofis 15 Nov 09, 2022