Browse SoTA > Medical > Drug Discovery

# Drug Discovery Edit

85 papers with code · Medical

Drug discovery is the task of applying machine learning to discover new candidate drugs.

( Image credit: Neural Graph Fingerprints )

# Self-Normalizing Neural Networks

We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.

1,486

# Neural Message Passing for Quantum Chemistry

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

926

# Gated Graph Sequence Neural Networks

17 Nov 2015Microsoft/gated-graph-neural-network-samples

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

926

# Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

29 Nov 2018molecularsets/moses

Generative models are becoming the tools of choice for the discovery of new molecules and materials.

381

# An Overview of Multi-Task Learning in Deep Neural Networks

15 Jun 2017HazyResearch/metal

Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery.

377

# Convolutional Networks on Graphs for Learning Molecular Fingerprints

We introduce a convolutional neural network that operates directly on graphs.

361

# JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code.

266

# Junction Tree Variational Autoencoder for Molecular Graph Generation

We evaluate our model on multiple tasks ranging from molecular generation to optimization.

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# Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

200

# DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening

19 Apr 2020kexinhuang12345/DeepPurpose

The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes.

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