:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lee, Changhun, Lee, Gyumin
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Biomolecules
Online Access:	https://arxiv.org/abs/2407.11439
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TAGMol: Target-Aware Gradient-guided Molecule Generation
by: Dorna, Vineeth, et al.
Published: (2024)

TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
by: Yoo, Kiwoong, et al.
Published: (2024)

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

Unraveling the Potential of Diffusion Models in Small Molecule Generation
by: Zhang, Peining, et al.
Published: (2025)

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

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

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

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

Pesti-Gen: Unleashing a Generative Molecule Approach for Toxicity Aware Pesticide Design
by: Kim, Taehan, et al.
Published: (2025)

Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
by: Smith, Andrew, et al.
Published: (2025)

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

Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders
by: Liu, Hui, et al.
Published: (2025)

Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models
by: Zheng, Xinzhe, et al.
Published: (2025)

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

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
by: Lin, Haitao, et al.
Published: (2024)

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

RTMol: Rethinking Molecule-text Alignment in a Round-trip View
by: Chen, Letian, et al.
Published: (2025)

Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
by: Zhong, Zhiqiang, et al.
Published: (2025)

WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
by: Liu, Yunchao, et al.
Published: (2024)

ControllableGPT: A Ground-Up Designed Controllable GPT for Molecule Optimization
by: Liu, Xuefeng, et al.
Published: (2025)

A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning
by: Jang, Hyosoon, et al.
Published: (2026)

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

A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
by: Liu, Shengchao, et al.
Published: (2023)

Text-guided Diffusion Model for 3D Molecule Generation
by: Luo, Yanchen, et al.
Published: (2024)

A Foundation Chemical Language Model for Comprehensive Fragment-Based Drug Discovery
by: Ho, Alexander, et al.
Published: (2025)

MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation
by: Xu, Dong, et al.
Published: (2025)

Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship
by: Ko, Eunji, et al.
Published: (2024)

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

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

Do Protein Transformers Have Biological Intelligence?
by: Lin, Fudong, et al.
Published: (2025)

Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
by: Wang, Yiquan, et al.
Published: (2025)

Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning
by: Li, Jiahan, et al.
Published: (2022)

Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery
by: Lan, Jing, et al.
Published: (2025)

Transformers trained on proteins can learn to attend to Euclidean distance
by: Ellmen, Isaac, et al.
Published: (2025)

Can Large Language Models Understand Molecules?
by: Sadeghi, Shaghayegh, et al.
Published: (2024)

Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery
by: Lan, Jing, et al.
Published: (2025)

Towards a Better Model with Dual Transformer for Drug Response Prediction
by: Li, Kun, et al.
Published: (2022)

SEISMO: Increasing Sample Efficiency in Molecular Optimization with a Trajectory-Aware LLM Agent
by: Krüger, Fabian P., et al.
Published: (2026)

RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models
by: Zhou, Yanlin, et al.
Published: (2024)

pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2
by: Chae, Joongwon, et al.
Published: (2024)