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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 401–425 of 691 papers

TitleStatusHype
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models—0
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?—0
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks—0
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model—0
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Chemical Property Prediction Under Experimental Biases—0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Complete Neural Networks for Complete Euclidean Graphs—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
Continuous Representation of Molecules Using Graph Variational Autoencoder—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction—0
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