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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 651–691 of 691 papers

TitleStatusHype
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
BayesGrad: Explaining Predictions of Graph Convolutional NetworksCode0
Positional Encoding meets Persistent Homology on GraphsCode0
Unsupervised Musical Object Discovery from AudioCode0
Splicing Up Your Predictions with RNA Contrastive LearningCode0
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization ProblemsCode0
Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property PredictionCode0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Graph Anisotropic DiffusionCode0
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement LearningCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studyCode0
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation LearningCode0
GeomCLIP: Contrastive Geometry-Text Pre-training for MoleculesCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
GenIC: An LLM-Based Framework for Instance Completion in Knowledge GraphsCode0
Subgraph Aggregation for Out-of-Distribution Generalization on GraphsCode0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
Attentive Walk-Aggregating Graph Neural NetworksCode0
GCI: A (G)raph (C)oncept (I)nterpretation FrameworkCode0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformationCode0
Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property PredictionCode0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and ClassificationCode0
Controlled Molecule Generator for Optimizing Multiple Chemical PropertiesCode0
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Ranking Structured Objects with Graph Neural NetworksCode0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Uncertainty quantification for predictions of atomistic neural networksCode0
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