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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 501–550 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
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
Triangular Contrastive Learning on Molecular Graphs—0
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence—0
Two-Stage Pretraining for Molecular Property Prediction in the Wild—0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
TxGemma: Efficient and Agentic LLMs for Therapeutics—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
Uncertainty quantification of molecular property prediction with Bayesian neural networks—0
Synergistic Benefits of Joint Molecule Generation and Property Prediction—0
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules—0
Uni-Mol: A Universal 3D Molecular Representation Learning Framework—0
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction—0
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