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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 176–200 of 691 papers

TitleStatusHype
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
Dual-view Molecule Pre-trainingCode1
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
Molecule Attention TransformerCode1
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic InsightsCode1
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Multiset-Equivariant Set Prediction with Approximate Implicit DifferentiationCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representationsCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informaticsCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Learning Invariances in Neural NetworksCode1
Optimal Transport Graph Neural NetworksCode1
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