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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 501–550 of 691 papers

TitleStatusHype
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
A graph representation of molecular ensembles for polymer property predictionCode1
Partial Product Aware Machine Learning on DNA-Encoded Libraries—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Graph Anisotropic DiffusionCode0
Learning to Split for Automatic Bias DetectionCode1
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Automatic Identification of Chemical Moieties—0
Protein Representation Learning by Geometric Structure PretrainingCode2
Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptorsCode1
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Structured Multi-task Learning for Molecular Property PredictionCode1
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug DiscoveryCode3
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal TransportCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative GamesCode1
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Improving VAE based molecular representations for compound property predictionCode1
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation—0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
Pairwise Learning for Neural Link PredictionCode1
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing—0
Molecular Contrastive Learning with Chemical Element Knowledge GraphCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Multiset-Equivariant Set Prediction with Approximate Implicit DifferentiationCode1
Image-Like Graph Representations for Improved Molecular Property Prediction—0
Directional Message Passing on Molecular Graphs via Synthetic Coordinates—0
SPECTRe: Substructure Processing, Enumeration, and Comparison Tool Resource: An efficient tool to encode all substructures of molecules represented in SMILES—0
Extracting Material Property Measurement Data from Scientific Articles—0
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks—0
Geometric Transformer for End-to-End Molecule Properties PredictionCode1
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Why Propagate Alone? Parallel Use of Labels and Features on GraphsCode1
Relative Molecule Self-Attention TransformerCode1
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