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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 651–675 of 691 papers

TitleStatusHype
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints—0
Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery—0
SPECTRe: Substructure Processing, Enumeration, and Comparison Tool Resource: An efficient tool to encode all substructures of molecules represented in SMILES—0
Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech—0
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients—0
Structural Property Prediction—0
Structure-based drug design with geometric deep learning—0
Substructure-Atom Cross Attention for Molecular Representation Learning—0
Supervised Pretraining for Material Property Prediction—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites—0
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction—0
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining—0
Task Addition in Multi-Task Learning by Geometrical Alignment—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model—0
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks—0
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey—0
T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction—0
Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates—0
Token-Mol 1.0: Tokenized drug design with large language model—0
Topological Neural Networks go Persistent, Equivariant, and Continuous—0
Towards Interpretable Molecular Graph Representation Learning—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
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