Molecular Property Prediction

49 papers with code • 0 benchmarks • 3 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Neural Message Passing for Quantum Chemistry

brain-research/mpnn ICML 2017

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

Analyzing Learned Molecular Representations for Property Prediction

swansonk14/chemprop 2 Apr 2019

In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.

Strategies for Pre-training Graph Neural Networks

snap-stanford/pretrain-gnns ICLR 2020

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

fanyun-sun/InfoGraph ICLR 2020

There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.

Path-Augmented Graph Transformer Network

benatorc/PA-Graph-Transformer 29 May 2019

Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

awslabs/dgl-lifesci 25 Jun 2019

In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.

Optimal Transport Graph Neural Networks

benatorc/OTGNN 8 Jun 2020

Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.

Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

biomed-AI/MolRep 1 Jul 2021

Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.

Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning

zoisboukouvalas/pyiva 1 Nov 2018

Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.

testRNN: Coverage-guided Testing on Recurrent Neural Networks

TrustAI/testRNN 20 Jun 2019

Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction.