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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 671–680 of 691 papers

TitleStatusHype
Deep Robust Subjective Visual Property Prediction in Crowdsourcing—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
Uncertainty quantification of molecular property prediction using Bayesian neural network models—0
Graph Convolutional Neural Networks for Polymers Property Prediction—0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine LearningCode0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media—0
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of CompoundsCode0
BayesGrad: Explaining Predictions of Graph Convolutional NetworksCode0
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