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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 376–400 of 691 papers

TitleStatusHype
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
All You Need Is Synthetic Task Augmentation—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
A molecular hypergraph convolutional network with functional group information—0
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
Artificial Intelligence Enabled Material Behavior Prediction—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Atom-Motif Contrastive Transformer for Molecular Property Prediction—0
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
Automatic Identification of Chemical Moieties—0
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models—0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
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