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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
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
Relative Molecule Self-Attention TransformerCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informaticsCode1
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—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
Artificial Intelligence Enabled Material Behavior Prediction—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Graph Convolution: A High-Order and Adaptive Approach—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
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
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
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