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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 201–225 of 691 papers

TitleStatusHype
Relative Molecule Self-Attention TransformerCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Geometric Transformer for End-to-End Molecule Properties PredictionCode1
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised LearningCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property PredictionCode1
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Complete Neural Networks for Complete Euclidean Graphs—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Artificial Intelligence Enabled Material Behavior Prediction—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—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
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
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
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