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Bibliografiska uppgifter
Huvudupphovsmän:	Dunn, Ian, Koes, David Ryan
Materialtyp:	Preprint
Publicerad:	2024
Ämnen:	Biomolecules Machine Learning
Länkar:	https://arxiv.org/abs/2404.19739
Taggar:	Lägg till en tagg Inga taggar, Lägg till första taggen!

Liknande verk

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

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

Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure
av: Dunn, Ian, et al.
Publicerad: (2023)

Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
av: Peñaherrera, Daniel, et al.
Publicerad: (2026)

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

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

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

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

t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generation
av: Wu, Juan-Ni, et al.
Publicerad: (2023)

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

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

Multi-domain Distribution Learning for De Novo Drug Design
av: Schneuing, Arne, et al.
Publicerad: (2025)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
av: Huguet, Guillaume, et al.
Publicerad: (2024)

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

Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
av: Ma, Zheng, et al.
Publicerad: (2024)

Flexible MOF Generation with Torsion-Aware Flow Matching
av: Kim, Nayoung, et al.
Publicerad: (2025)

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

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
av: Zhang, Xiang, et al.
Publicerad: (2025)

AlphaFold Meets Flow Matching for Generating Protein Ensembles
av: Jing, Bowen, et al.
Publicerad: (2024)

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

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
av: Anand, Rishabh, et al.
Publicerad: (2024)

A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design
av: Gharbi, Yossra, et al.
Publicerad: (2024)

AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
av: Xia, Jun, et al.
Publicerad: (2024)

BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation
av: Yalabadi, Ali Khodabandeh, et al.
Publicerad: (2025)

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

RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching
av: Gao, Letian, et al.
Publicerad: (2024)

Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
av: Tang, Sophia, et al.
Publicerad: (2025)

Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
av: Song, Yuxuan, et al.
Publicerad: (2024)

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

Dirichlet Flow Matching with Applications to DNA Sequence Design
av: Stark, Hannes, et al.
Publicerad: (2024)

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

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

Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
av: Xu, Can, et al.
Publicerad: (2024)

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

NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
av: Liu, Zhiyuan, et al.
Publicerad: (2025)

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

GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
av: Yan, Shaoheng, et al.
Publicerad: (2025)

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

Attention Based Molecule Generation via Hierarchical Variational Autoencoder
av: Sivanesan, Divahar
Publicerad: (2024)

Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
av: Chen, Tong, et al.
Publicerad: (2025)