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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 201–250 of 691 papers

TitleStatusHype
Learning Harmonic Molecular Representations on Riemannian ManifoldCode1
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Structured Multi-task Learning for Molecular Property PredictionCode1
Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic StudyCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational GraphsCode1
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Generative Deep Learning Framework for Inverse Design of Fuels—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Guided Latent Slot Diffusion for Object-Centric Learning—0
Complete Neural Networks for Complete Euclidean Graphs—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
Gaussian Process Molecule Property Prediction with FlowMO—0
G^3: Representation Learning and Generation for Geometric Graphs—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Graph Residual based Method for Molecular Property Prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Neural Networks Go Forward-Forward—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Extracting Material Property Measurement Data from Scientific Articles—0
Artificial Intelligence Enabled Material Behavior Prediction—0
Explanatory Masks for Neural Network Interpretability—0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Adaptive Invariance for Molecule Property Prediction—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
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