Molecular Property Prediction
121 papers with code • 18 benchmarks • 19 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
QM7
SIDER
Subtasks
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.
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.
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.
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
bingsu12/momu
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Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
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.
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
seyonechithrananda/bert-loves-chemistry
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GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
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.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
