molecular representation
60 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in molecular representation
Libraries
Use these libraries to find molecular representation models and implementationsMost implemented papers
A Systematic Survey of Chemical Pre-trained Models
junxia97/awesome-pretrain-on-molecules
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Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
Yinghao-Li/UncertaintyBenchmark
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While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction
bbrighttaer/jova_baselines
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In this study, we propose a self-attention-based multi-view representation learning approach for modeling drug-target interactions.
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
zabaras/GSVAE
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In this work, we assess the predictive capabilities of a molecular generative model developed based on variational inference and graph theory in the small data regime.
TrimNet: learning molecular representation from triplet messages for biomedicine
yvquanli/TrimNet
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These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design
Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as Ollivier persistent Ricci curvature.
Few-Shot Graph Learning for Molecular Property Prediction
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.
Molecular Representation Learning by Leveraging Chemical Information
PaddlePaddle/PaddleHelix
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NA 2021
As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph.
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
PKUterran/HamNet
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Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
