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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 451–500 of 691 papers

TitleStatusHype
In-Context Learning for Few-Shot Molecular Property Prediction—0
Splicing Up Your Predictions with RNA Contrastive LearningCode0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Atom-Motif Contrastive Transformer for Molecular Property Prediction—0
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Molecule Design by Latent Prompt Transformer—0
Towards out-of-distribution generalizable predictions of chemical kinetics propertiesCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
Language models in molecular discovery—0
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
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
Motif-aware Attribute Masking for Molecular Graph Pre-trainingCode0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
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
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
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Structural Property Prediction—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer GenerationCode0
Molecular geometric deep learningCode0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Self-supervised Learning and Graph Classification under Heterophily—0
MUBen: Benchmarking the Uncertainty of Molecular Representation ModelsCode0
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Simplicial Message Passing for Chemical Property Prediction—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Set-based Neural Network Encoding Without Weight Tying—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
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