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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 326–350 of 691 papers

TitleStatusHype
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingCode1
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
ADMET property prediction through combinations of molecular fingerprintsCode1
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
Language models in molecular discovery—0
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural NetworkCode2
Temporal graph models fail to capture global temporal dynamicsCode0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
DYMAG: Rethinking Message Passing Using Dynamical-systems-based WaveformsCode0
Motif-aware Attribute Masking for Molecular Graph Pre-trainingCode0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational GraphsCode1
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
On Data Imbalance in Molecular Property Prediction with Pre-training—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Evaluating the diversity and utility of materials proposed by generative models—0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks—0
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point CloudsCode0
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