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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
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties—0
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media—0
Molecular Property Prediction Based on Graph Structure Learning—0
Molecular Property Prediction by Semantic-invariant Contrastive Learning—0
Molecular Representation Learning by Leveraging Chemical Information—0
Molecular topological deep learning for polymer property prediction—0
Molecule Design by Latent Prompt Transformer—0
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
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