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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 101–125 of 691 papers

TitleStatusHype
Molecular Graph Contrastive Learning with Line GraphCode0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Graph Generative Pre-trained Transformer—0
Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery—0
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
Data-Driven Self-Supervised Graph Representation LearningCode0
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic InsightsCode1
Category-Specific Topological Learning of Metal-Organic Frameworks—0
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property—0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representationsCode1
M^3-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and DiscoveryCode2
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates—0
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials—0
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
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