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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 401–450 of 691 papers

TitleStatusHype
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Invariance-Aware Randomized Smoothing Certificates—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts—0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property—0
Language models in molecular discovery—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Learning Invariances in Neural Networks from Training Data—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
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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