SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–310 of 691 papers

TitleStatusHype
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent—0
Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative TasksCode0
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds—0
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property PredictionCode1
UniMAP: Universal SMILES-Graph Representation LearningCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Show:102550
← PrevPage 31 of 70Next →

No leaderboard results yet.