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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
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Invariance-Aware Randomized Smoothing Certificates—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts—0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property—0
Language models in molecular discovery—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Learning Invariances in Neural Networks from Training Data—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
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