SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 691 papers

TitleStatusHype
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
G^3: Representation Learning and Generation for Geometric Graphs—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
Generative Deep Learning Framework for Inverse Design of Fuels—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
Graph Convolution: A High-Order and Adaptive Approach—0
Graph Convolutional Neural Networks for Polymers Property Prediction—0
Graph Generative Pre-trained Transformer—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Graph Networks with Spectral Message Passing—0
Graph Neural Network Architecture Search for Molecular Property Prediction—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Graph Neural Networks for Molecules—0
Graph neural networks for the prediction of molecular structure-property relationships—0
Graph Neural Networks Go Forward-Forward—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Residual based Method for Molecular Property Prediction—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Guided Latent Slot Diffusion for Object-Centric Learning—0
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
Heat Kernel Goes Topological—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Image-Like Graph Representations for Improved Molecular Property Prediction—0
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification—0
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Invariance-Aware Randomized Smoothing Certificates—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Show:102550
← PrevPage 8 of 14Next →

No leaderboard results yet.