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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 301–325 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
Improving Molecular Properties Prediction Through Latent Space FusionCode2
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
Splicing Up Your Predictions with RNA Contrastive LearningCode0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Atom-Motif Contrastive Transformer for Molecular Property Prediction—0
Lo-Hi: Practical ML Drug Discovery BenchmarkCode1
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
Molecule Design by Latent Prompt Transformer—0
Towards out-of-distribution generalizable predictions of chemical kinetics propertiesCode0
On the Stability of Expressive Positional Encodings for GraphsCode1
AstroCLIP: A Cross-Modal Foundation Model for GalaxiesCode1
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