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Drug Discovery

23 papers with code · Medical

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

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Greatest papers with code

Self-Normalizing Neural Networks

NeurIPS 2017 bioinf-jku/SNNs

However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.


Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

30 Mar 2017deepchem/deepchem

The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features.


Neural Message Passing for Quantum Chemistry

ICML 2017 Microsoft/gated-graph-neural-network-samples

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature.


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. In this work, we study feature learning techniques for graph-structured inputs.

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Convolutional Networks on Graphs for Learning Molecular Fingerprints

NeurIPS 2015 HIPS/neural-fingerprint

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.


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

29 Nov 2018molecularsets/moses

Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials. In this work, we introduce MOlecular SEtS (MOSES), a benchmarking platform to support research on machine learning for drug discovery.


Junction Tree Variational Autoencoder for Molecular Graph Generation

ICML 2018 wengong-jin/icml18-jtnn

In computational terms, this task involves continuous embedding and generation of molecular graphs. We evaluate our model on multiple tasks ranging from molecular generation to optimization.


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. This article aims to give a general overview of MTL, particularly in deep neural networks.


Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks

20 Oct 2017gnina/gnina

Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization. A scoring function that is differentiable with respect to atom positions can be used for both scoring and gradient-based optimization of poses for docking.


Protein-Ligand Scoring with Convolutional Neural Networks

8 Dec 2016gnina/gnina

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.