automatic identification to do drug target binding affinity (regression) or drug target interaction prediction (binary) task.support DTI, DDI, PPI, molecular property prediction, protein function predictions!.All of these under 10 lines but with lots of flexibility! Switching encoding is as simple as changing the encoding names! Now supports hyperparameter tuning via Bayesian Optimization through the Ax platform! A demo is provided in here.ġ5+ powerful encodings for drugs and proteins, ranging from deep neural network on classic cheminformatics fingerprints, CNN, transformers to message passing graph neural network, with 50+ combined models! Most of the combinations of the encodings are not yet in existing works.Support drug property prediction for screening data that does not have target proteins such as bacteria! An example using RDKit2D with DNN for training and repurposing for pseudomonas aeruginosa (MIT AI Cures's open task) is provided as a demo.Two tutorials are online to go through DeepPurpose's framework to do drug-target interaction prediction and drug property prediction ( DTI, Drug Property).A blog is posted on the Towards Data Science Medium column, check this out!.A simple web UI for DTI prediction can be created under 10 lines using Gradio! A demo is provided here.DeepPurpose has now supported three more tasks: DDI, PPI and Protein Function Prediction! You can simply call from DeepPurpose import DDI/PPI/ProteinPred to use, checkout examples below!.Using DeepPurpose, we made a humans-in-the-loop molecular design web UI interface, check it out!.Google Colab Installation Instructions are provided here.Added 5 more pretrained models on BindingDB IC50 Units (around 1Million data points).DeepPurpose is published in Bioinformatics!.DeepPurpose can now be installed via pip!.DeepPurpose is now supported by TDC data loader, which contains a large collection of ML for therapeutics datasets, including many drug property, DTI datasets.0.1.2 Support 5 new graph neural network based models for compound encoding (DGL_GCN, DGL_NeuralFP, DGL_GIN_AttrMasking, DGL_GIN_ContextPred, DGL_AttentiveFP), implemented using DGL Life Science! An example is provided here!.It allows very easy usage (several lines of codes only) to facilitate deep learning for life science research. We focus on DTI and its applications in Drug Repurposing and Virtual Screening, but support various other molecular encoding tasks. This repository hosts DeepPurpose, a Deep Learning Based Molecular Modeling and Prediction Toolkit on Drug-Target Interaction Prediction, Compound Property Prediction, Protein-Protein Interaction Prediction, and Protein Function prediction (using PyTorch). A Deep Learning Library for Compound and Protein ModelingĭTI, Drug Property, PPI, DDI, Protein Function PredictionĪpplications in Drug Repurposing, Virtual Screening, QSAR, Side Effect Prediction and More
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