Copertina

t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wu, Juan-Ni, Wang, Tong, Chen, Yue, Tang, Li-Juan, Wu, Hai-Long, Yu, Ru-Qin
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence Biomolecules
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation
di: Wu, Juan-Ni, et al.
Pubblicazione: (2024)

UniMAP: Universal SMILES-Graph Representation Learning
di: Feng, Shikun, et al.
Pubblicazione: (2023)

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

The Dance of Atoms-De Novo Protein Design with Diffusion Model
di: Qin, Yujie, et al.
Pubblicazione: (2025)

Fragment-Masked Diffusion for Molecular Optimization
di: Li, Kun, et al.
Pubblicazione: (2024)

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
di: Gaiński, Piotr, et al.
Pubblicazione: (2025)

PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
di: Tang, Sophia, et al.
Pubblicazione: (2024)

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision
di: Zheng, Kangjie, et al.
Pubblicazione: (2024)

Reaction-conditioned De Novo Enzyme Design with GENzyme
di: Hua, Chenqing, et al.
Pubblicazione: (2024)

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

Applications of Modular Co-Design for De Novo 3D Molecule Generation
di: Reidenbach, Danny, et al.
Pubblicazione: (2025)

Contextual Molecule Representation Learning from Chemical Reaction Knowledge
di: Tang, Han, et al.
Pubblicazione: (2024)

Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation
di: Tang, Haocheng, et al.
Pubblicazione: (2025)

Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
di: Chen, Dingshuo, et al.
Pubblicazione: (2024)

Conditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules
di: Islam, Naeyma N., et al.
Pubblicazione: (2026)

Open-Source Molecular Processing Pipeline for Generating Molecules
di: Shreyas, V, et al.
Pubblicazione: (2024)

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

p2smi: A Python Toolkit for Peptide FASTA-to-SMILES Conversion and Molecular Property Analysis
di: Feller, Aaron L., et al.
Pubblicazione: (2025)

PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary
di: Li, Kun, et al.
Pubblicazione: (2026)

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

FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
di: Liu, Xuefeng, et al.
Pubblicazione: (2025)

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
di: Dunn, Ian, et al.
Pubblicazione: (2024)

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

MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning
di: Zhu, Yihan, et al.
Pubblicazione: (2025)

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
di: Qiu, Keyue, et al.
Pubblicazione: (2024)

ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
di: Zhou, Renyi, et al.
Pubblicazione: (2025)

FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation
di: Dunn, Ian, et al.
Pubblicazione: (2025)

UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
di: Feng, Shikun, et al.
Pubblicazione: (2024)

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

STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation
di: Kwon, Bum Chul, et al.
Pubblicazione: (2025)

AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy
di: Ding, Yan, et al.
Pubblicazione: (2023)

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

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

Discontinuous Epitope Fragments as Sufficient Target Templates for Efficient Binder Design
di: Deng, Zhenfeng, et al.
Pubblicazione: (2025)

Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
di: Li, Jiahan, et al.
Pubblicazione: (2024)

UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
di: Feng, Shikun, et al.
Pubblicazione: (2024)

AbGPT: De Novo Antibody Design via Generative Language Modeling
di: Kuan, Desmond, 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)

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
di: Lv, Changze, et al.
Pubblicazione: (2025)

Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design
di: Srinivas, Sakhinana Sagar, et al.
Pubblicazione: (2024)