Copertina

Molecule Design by Latent Prompt Transformer

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kong, Deqian, Huang, Yuhao, Xie, Jianwen, Wu, Ying Nian
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Biomolecules
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Molecule Design by Latent Prompt Transformer
di: Kong, Deqian, et al.
Pubblicazione: (2024)

UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
di: Kong, Xiangzhe, et al.
Pubblicazione: (2025)

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

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

MUDiff: Unified Diffusion for Complete Molecule Generation
di: Hua, Chenqing, et al.
Pubblicazione: (2023)

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
di: Cretu, Miruna, et al.
Pubblicazione: (2024)

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
di: Lin, Haitao, et al.
Pubblicazione: (2023)

SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
di: Huang, Yanwen, et al.
Pubblicazione: (2024)

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

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
di: Lin, Haitao, et al.
Pubblicazione: (2022)

Deep Learning for Protein Complex Prediction and Design
di: Xie, Ziwei
Pubblicazione: (2026)

Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates
di: Gao, Kaiyuan, et al.
Pubblicazione: (2024)

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

Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
di: Arthur-Loui, Heath, et al.
Pubblicazione: (2024)

Repurformer: Transformers for Repurposing-Aware Molecule Generation
di: Lee, Changhun, et al.
Pubblicazione: (2024)

Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
di: Ünlü, Atabey, et al.
Pubblicazione: (2023)

Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation
di: N, Siddartha Reddy, et al.
Pubblicazione: (2024)

Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets
di: Sob, Ulrich A. Mbou, et al.
Pubblicazione: (2024)

Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
di: Qiang, Bo, et al.
Pubblicazione: (2023)

Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities
di: Li, Kun, et al.
Pubblicazione: (2025)

Fast and Accurate Antibody Sequence Design via Structure Retrieval
di: Zhang, Xingyi, et al.
Pubblicazione: (2025)

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

Representing Molecules as Random Walks Over Interpretable Grammars
di: Sun, Michael, et al.
Pubblicazione: (2024)

Towards More Accurate Full-Atom Antibody Co-Design
di: Wu, Jiayang, et al.
Pubblicazione: (2025)

Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
di: McNutt, Andrew T., et al.
Pubblicazione: (2024)

Cell Morphology-Guided Small Molecule Generation with GFlowNets
di: Lu, Stephen Zhewen, et al.
Pubblicazione: (2024)

Attention Based Molecule Generation via Hierarchical Variational Autoencoder
di: Sivanesan, Divahar
Pubblicazione: (2024)

GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
di: Xiao, Teng, et al.
Pubblicazione: (2024)

Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
di: Kong, Deqian, et al.
Pubblicazione: (2024)

Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
di: Liu, Ningfeng, et al.
Pubblicazione: (2024)

MolPIF: A Parameter Interpolation Flow Model for Molecule Generation
di: Jin, Yaowei, et al.
Pubblicazione: (2025)

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

Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
di: Jolicoeur-Martineau, Alexia, et al.
Pubblicazione: (2024)

Exploring Discrete Flow Matching for 3D De Novo Molecule Generation
di: Dunn, Ian, et al.
Pubblicazione: (2024)

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction
di: Telepov, Alexander, et al.
Pubblicazione: (2024)

A Review on Fragment-based De Novo 2D Molecule Generation
di: Voloboev, Sergei
Pubblicazione: (2024)

A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation
di: Tang, Xiangru, et al.
Pubblicazione: (2024)

Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization
di: Liu, Zequn, et al.
Pubblicazione: (2026)

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
di: Daigavane, Ameya, et al.
Pubblicazione: (2023)

S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
di: Zhou, Gengmo, et al.
Pubblicazione: (2024)