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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 676–691 of 691 papers

TitleStatusHype
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformationCode0
Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property PredictionCode0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and ClassificationCode0
Controlled Molecule Generator for Optimizing Multiple Chemical PropertiesCode0
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Ranking Structured Objects with Graph Neural NetworksCode0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Uncertainty quantification for predictions of atomistic neural networksCode0
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