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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 426–450 of 691 papers

TitleStatusHype
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumerationCode1
Towards deep generation of guided wave representations for composite materialsCode0
Implicit Convolutional Kernels for Steerable CNNsCode1
An open unified deep graph learning framework for discovering drug leadsCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
Invariance-Aware Randomized Smoothing Certificates—0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Molecular Joint Representation Learning via Multi-modal Information—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation ModelCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Equivariant Networks for Crystal Structures—0
ParticleGrid: Enabling Deep Learning using 3D Representation of MaterialsCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
A Systematic Survey of Chemical Pre-trained ModelsCode2
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