Molecular Property Prediction
185 papers with code • 18 benchmarks • 20 datasets
Molecular property prediction is the task of predicting the properties of a molecule from its structure.
Benchmarks
These leaderboards are used to track progress in Molecular Property Prediction
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Libraries
Use these libraries to find Molecular Property Prediction models and implementationsDatasets
MoleculeNet
QM9
Tox21
BBBP (Blood-Brain Barrier Penetration)
SIDER
Subtasks
Most implemented papers
Graph Attention Networks
PetarV-/GAT
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ICLR 2018
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Neural Message Passing for Quantum Chemistry
brain-research/mpnn
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ICML 2017
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
How Powerful are Graph Neural Networks?
weihua916/powerful-gnns
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ICLR 2019
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Strategies for Pre-training Graph Neural Networks
snap-stanford/pretrain-gnns
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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.
Principal Neighbourhood Aggregation for Graph Nets
lukecavabarrett/pna
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NeurIPS 2020
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
How Attentive are Graph Attention Networks?
tech-srl/how_attentive_are_gats
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ICLR 2022
Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Innixma/autogluon-benchmarking
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We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
MoleculeNet: A Benchmark for Molecular Machine Learning
However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
fanyun-sun/InfoGraph
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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.
