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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 501–525 of 691 papers

TitleStatusHype
Self-supervised learning for crystal property prediction via denoising—0
SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Set-based Neural Network Encoding Without Weight Tying—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
Simplicial Message Passing for Chemical Property Prediction—0
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
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
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