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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 76–100 of 691 papers

TitleStatusHype
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Directed Graph Grammars for Sequence-based LearningCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
E(n) Equivariant Topological Neural NetworksCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
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