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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 421–430 of 691 papers

TitleStatusHype
Accelerating Material Property Prediction using Generically Complete Isometry InvariantsCode0
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language ModelsCode0
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studyCode0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization ProblemsCode0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
Molecular Property Prediction Based on Graph Structure Learning—0
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