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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 576–600 of 691 papers

TitleStatusHype
MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing—0
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction—0
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization—0
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning—0
MoMa: A Modular Deep Learning Framework for Material Property Prediction—0
Multi-modal cascade feature transfer for polymer property prediction—0
Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery—0
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures—0
Multiparameter Persistent Homology for Molecular Property Prediction—0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
Neural Message Passing on High Order Paths—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
On Data Imbalance in Molecular Property Prediction with Pre-training—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions—0
Optimal Decision Making in High-Throughput Virtual Screening Pipelines—0
Orbital Graph Convolutional Neural Network for Material Property Prediction—0
Out-of-distribution materials property prediction using adversarial learning based fine-tuning—0
Partial Product Aware Machine Learning on DNA-Encoded Libraries—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction—0
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation—0
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