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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 551–600 of 691 papers

TitleStatusHype
Uni-Mol: A Universal 3D Molecular Representation Learning Framework—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Path-aware Siamese Graph Neural Network for Link PredictionCode0
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction—0
Graph neural networks for the prediction of molecular structure-property relationships—0
Uncertainty quantification for predictions of atomistic neural networksCode0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Affinity-Aware Graph Networks—0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
3D Graph Contrastive Learning for Molecular Property Prediction—0
Embedding Graphs on Grassmann ManifoldCode0
Triangular Contrastive Learning on Molecular Graphs—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
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
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
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
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
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
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
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
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing—0
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
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Attentive Walk-Aggregating Graph Neural NetworksCode0
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