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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 526–550 of 691 papers

TitleStatusHype
Towards deep generation of guided wave representations for composite materialsCode0
An open unified deep graph learning framework for discovering drug leadsCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Invariance-Aware Randomized Smoothing Certificates—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Molecular Joint Representation Learning via Multi-modal Information—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
Equivariant Networks for Crystal Structures—0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property PredictionCode0
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction ClassificationCode0
Structure-based drug design with geometric deep learning—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
Substructure-Atom Cross Attention for Molecular Representation Learning—0
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
Improving Molecular Pretraining with Complementary FeaturizationsCode0
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Graph Neural Networks for Molecules—0
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients—0
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