Molecular Property Prediction
83 papers with code • 18 benchmarks • 8 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 implementations
MoleculeNet
QM9
Tox21
SIDER
AIDS Antiviral Screen
Molecule3D
Most implemented papers
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.
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.
Analyzing Learned Molecular Representations for Property Prediction
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.
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.
Self-Supervised Graph Transformer on Large-Scale Molecular Data
tencent-ailab/grover
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NeurIPS 2020
We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
Path-Augmented Graph Transformer Network
benatorc/PA-Graph-Transformer
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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
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
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Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
