Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Shengli, Qin, Shiyi, Van Lehn, Reid C., Balaprakash, Prasanna, Zavala, Victor M.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Chemical Physics Biomolecules
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
by: Wang, Zhuoyuan, et al.
Published: (2023)

Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
by: Xiang, Yuran, et al.
Published: (2024)

The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
by: Youngmin, Lee, et al.
Published: (2024)

Two-Stage Pretraining for Molecular Property Prediction in the Wild
by: Wijaya, Kevin Tirta, et al.
Published: (2024)

Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
by: Liyaqat, Tanya, et al.
Published: (2024)

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
by: Liu, Gang, et al.
Published: (2024)

EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction
by: Tian, Qingwen, et al.
Published: (2024)

E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction
by: Mitnikov, Ilan, et al.
Published: (2024)

NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
by: Qiao, Zhuoran, et al.
Published: (2024)

GP-MoLFormer: A Foundation Model For Molecular Generation
by: Ross, Jerret, et al.
Published: (2024)

GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
by: Shen, Yuchen, et al.
Published: (2024)

GraphXForm: Graph transformer for computer-aided molecular design
by: Pirnay, Jonathan, et al.
Published: (2024)

RGFN: Synthesizable Molecular Generation Using GFlowNets
by: Koziarski, Michał, et al.
Published: (2024)

Pre-training of Molecular GNNs via Conditional Boltzmann Generator
by: Koge, Daiki, et al.
Published: (2023)

Transformers Discover Molecular Structure Without Graph Priors
by: Kreiman, Tobias, et al.
Published: (2025)

EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation
by: Yang, Yixuan, et al.
Published: (2025)

MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights
by: Hu, Jingjing, et al.
Published: (2024)

Training-Free Guidance for Discrete Diffusion Models for Molecular Generation
by: Kerby, Thomas J., et al.
Published: (2024)

Diffusion-Driven Generative Framework for Molecular Conformation Prediction
by: Yang, Bobin, et al.
Published: (2023)

De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning
by: Hou, Junyu
Published: (2025)

Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
by: Ali, Sarwan, et al.
Published: (2024)

Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
by: Lee, Chanhui, et al.
Published: (2025)

Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
by: Zhang, Kaiwei, et al.
Published: (2024)

Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
by: Zhang, He, et al.
Published: (2024)

Clever Hans in Chemistry: Chemist Style Signals Confound Activity Prediction on Public Benchmarks
by: Blevins, Andrew D., et al.
Published: (2025)

WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
by: Wang, Fanmeng, et al.
Published: (2024)

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
by: Min, Yaosen, et al.
Published: (2022)

Pre-trained Molecular Language Models with Random Functional Group Masking
by: Peng, Tianhao, et al.
Published: (2024)

Physics-informed generative model for drug-like molecule conformers
by: Williams, David C., et al.
Published: (2024)

Tokenization for Molecular Foundation Models
by: Wadell, Alexius, et al.
Published: (2024)

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
by: Yang, Soojung, et al.
Published: (2024)

Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
by: Park, Jiwoong, et al.
Published: (2024)

Superior Molecular Representations from Intermediate Encoder Layers
by: Pinto, Luis
Published: (2025)

Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
by: Murdeshwar, Sanya, et al.
Published: (2026)

Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
by: Huang, Yufei, et al.
Published: (2024)

Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
by: Zhang, Odin, et al.
Published: (2024)

Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
by: Zhang, Chengwei, et al.
Published: (2023)

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
by: Grambow, Colin A., et al.
Published: (2023)

Understanding active learning of molecular docking and its applications
by: Kim, Jeonghyeon, et al.
Published: (2024)

HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations
by: Angioletti, Daniele, et al.
Published: (2024)