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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

Showing 121130 of 691 papers

TitleStatusHype
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates0
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials0
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 Prediction0
SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions0
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and ClassificationCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning0
Two-Stage Pretraining for Molecular Property Prediction in the Wild0
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