molecular representation
64 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.
Can Large Language Models Understand Molecules?
Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks.
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
BrightBlueCheese/transformers_and_chemistry
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However, we observed that absolute validation loss is not a definitive indicator of model performance - contradicts previous research - at least for fine-tuning tasks: instead, model size plays a crucial role.
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.
