Cover Image

Towards Efficient Molecular Property Optimization with Graph Energy Based Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Miglior, Luca, Simone, Lorenzo, Podda, Marco, Bacciu, Davide
Format:	Preprint
Published:	2025
Subjects:	Biomolecules Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Classifier-free graph diffusion for molecular property targeting
by: Ninniri, Matteo, et al.
Published: (2023)

Towards Faster and More Compact Foundation Models for Molecular Property Prediction
by: Ghunaim, Yasir, et al.
Published: (2025)

Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction
by: Dablander, Markus
Published: (2024)

Multi-Objective Latent Space Optimization of Generative Molecular Design Models
by: Abeer, A N M Nafiz, et al.
Published: (2022)

Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
by: Zhong, Zhiqiang, et al.
Published: (2024)

Learning Hierarchical Interaction for Accurate Molecular Property Prediction
by: Hong, Huiyang, et al.
Published: (2025)

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
by: Peng, Xingang, et al.
Published: (2022)

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

DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
by: Zhou, Xiangxin, et al.
Published: (2024)

Molecular Generative Adversarial Network with Multi-Property Optimization
by: Tang, Huidong, et al.
Published: (2024)

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
by: Liu, Songtao, et al.
Published: (2024)

Graph Diffusion Transformers for Multi-Conditional Molecular Generation
by: Liu, Gang, et al.
Published: (2024)

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
by: Jiang, Shengli, et al.
Published: (2023)

Coder as Editor: Code-driven Interpretable Molecular Optimization
by: Zhu, Wenyu, et al.
Published: (2025)

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
by: He, Yinhan, et al.
Published: (2024)

Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
by: Jiao, Shujian, et al.
Published: (2024)

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
by: Wang, Danqing, et al.
Published: (2024)

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
by: Zhang, Xiangyu
Published: (2024)

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
by: Ketata, Mohamed Amine, et al.
Published: (2024)

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
by: Kong, Xiangzhe, et al.
Published: (2023)

Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
by: Wang, Zeyu, et al.
Published: (2024)

Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation
by: Shah, Aayush, et al.
Published: (2024)

GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
by: Yuan, Yiliang, et al.
Published: (2024)

Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
by: Guo, Jinjiang, et al.
Published: (2022)

Molecular Diffusion Models with Virtual Receptors
by: Halfon, Matan, et al.
Published: (2024)

Generative Modeling of Molecular Dynamics Trajectories
by: Jing, Bowen, et al.
Published: (2024)

It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design
by: Guo, Jeff, et al.
Published: (2024)

Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design
by: Guo, Jeff, et al.
Published: (2023)

GeoGraph: Geometric and Graph-based Ensemble Descriptors for Intrinsically Disordered Proteins
by: Quinn, Eoin, et al.
Published: (2025)

Benchmarking Large Language Models for Molecule Prediction Tasks
by: Zhong, Zhiqiang, et al.
Published: (2024)

A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
by: Rana, Md Masud, et al.
Published: (2025)

3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information
by: Kuang, Taojie, et al.
Published: (2023)

Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction
by: Lu, Xiaohua, et al.
Published: (2023)

DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
by: Yan, Jiaxian, et al.
Published: (2024)

Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
by: Bergues, Noémie, et al.
Published: (2025)

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
by: Kyro, Gregory W., et al.
Published: (2023)

NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries
by: Nowara, Ewa M., et al.
Published: (2024)

Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
by: Zhou, Xiangxin, et al.
Published: (2024)

Molecular Fingerprints Are Strong Models for Peptide Function Prediction
by: Adamczyk, Jakub, et al.
Published: (2025)

MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
by: Kapuśniak, Kacper, et al.
Published: (2025)