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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 226–250 of 691 papers

TitleStatusHype
Graph Residual based Method for Molecular Property Prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Neural Networks Go Forward-Forward—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Extracting Material Property Measurement Data from Scientific Articles—0
Artificial Intelligence Enabled Material Behavior Prediction—0
Explanatory Masks for Neural Network Interpretability—0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Adaptive Invariance for Molecule Property Prediction—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
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