A PyTorch Library for Accelerating 3D Deep Learning Research
- pytorch
- 3d-deep-learning
- neural-networks
- differentiable-rendering
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A PyTorch Library for Accelerating 3D Deep Learning Research
Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.
Message Passing Neural Networks for Molecule Property Prediction
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Brian is a free, open source simulator for spiking neural networks.
Python package for graph neural networks in chemistry and biology
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry
A Euclidean diffusion model for structure-based drug design.
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
Sequential regulatory activity predictions with deep convolutional neural networks.
Neural networks toolbox focused on medical image analysis
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
GraphDTA: Predicting drug-target binding affinity with graph neural networks
Source code for "DeepDTA: deep drug-target binding affinity prediction"
RNA-seq prediction with deep convolutional neural networks.
MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Axon/Myelin segmentation using Deep Learning
Cryptographic Dataset Generation & Modelling Framework