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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 601–650 of 691 papers

TitleStatusHype
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Polymer Informatics: Current Status and Critical Next Steps—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
PotentialNet for Molecular Property Prediction—0
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
Predicting performance-related properties of refrigerant based on tailored small-molecule functional group contribution—0
Prediction of properties of metal alloy materials based on machine learning—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
Pre-training of Molecular GNNs via Conditional Boltzmann Generator—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Pure Component Property Estimation Framework Using Explainable Machine Learning Methods—0
QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation—0
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries—0
QUBO-inspired Molecular Fingerprint for Chemical Property Prediction—0
Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks—0
Quotient Complex Transformer (QCformer) for Perovskite Data Analysis—0
Recent Developments in GNNs for Drug Discovery—0
ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction—0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
Representing Molecules as Random Walks Over Interpretable Grammars—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances—0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-supervised learning for crystal property prediction via denoising—0
SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Set-based Neural Network Encoding Without Weight Tying—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
Simplicial Message Passing for Chemical Property Prediction—0
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
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