3D molecular generation models expand chemical space exploration in drug design

YT Xiang, GY Huang, XX Shi, GF Hao, GF Yang - Drug Discovery Today, 2024 - Elsevier
Drug discovery is essential in human diseases but faces challenges because of the vast
chemical space. Molecular generation models have become powerful tools to accelerate …

Machine learning for predicting protein properties: A comprehensive review

Y Wang, Y Zhang, X Zhan, Y He, Y Yang, L Cheng… - Neurocomputing, 2024 - Elsevier
In the field of protein engineering, the function and structure of proteins are key to
understanding cellular mechanisms, biological evolution, and biodiversity. With the …

Efficient generation of protein pockets with PocketGen

Z Zhang, WX Shen, Q Liu, M Zitnik - Nature Machine Intelligence, 2024 - nature.com
Designing protein-binding proteins is critical for drug discovery. However, artificial-
intelligence-based design of such proteins is challenging due to the complexity of protein …

How Good are Current Pocket-Based 3D Generative Models?: The Benchmark Set and Evaluation of Protein Pocket-Based 3D Molecular Generative Models

H Liu, Y Qin, Z Niu, M Xu, J Wu, X Xiao… - Journal of Chemical …, 2024 - ACS Publications
The development of a three-dimensional (3D) molecular generative model based on protein
pockets has recently attracted a lot of attention. This type of model aims to achieve the …

TamGen: drug design with target-aware molecule generation through a chemical language model

K Wu, Y Xia, P Deng, R Liu, Y Zhang, H Guo… - Nature …, 2024 - nature.com
Generative drug design facilitates the creation of compounds effective against pathogenic
target proteins. This opens up the potential to discover novel compounds within the vast …

3DSMILES-GPT: 3D molecular pocket-based generation with token-only large language model

J Wang, H Luo, R Qin, M Wang, X Wan, M Fang… - Chemical …, 2025 - pubs.rsc.org
The generation of three-dimensional (3D) molecules based on target structures represents a
cutting-edge challenge in drug discovery. Many existing approaches often produce …

[HTML][HTML] Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

S Guan, G Wang - Artificial Intelligence Chemistry, 2024 - Elsevier
Abstract Drug Research and Development (R&D) is a complex and difficult process, and
current drug R&D faces the challenges of long time span, high investment, and high failure …

Flexsbdd: Structure-based drug design with flexible protein modeling

Z Zhang, M Wang, Q Liu - arXiv preprint arXiv:2409.19645, 2024 - arxiv.org
Structure-based drug design (SBDD), which aims to generate 3D ligand molecules binding
to target proteins, is a fundamental task in drug discovery. Existing SBDD methods typically …

ECloudGen: Leveraging Electron Clouds as a Latent Variable to Scale Up Structure-based Molecular Design

O Zhang, J Jin, Z Wu, J Zhang, P Yuan, H Lin, H Zhong… - bioRxiv, 2024 - biorxiv.org
Abstract Structure-based molecule generation represents a significant advancement in AI-
aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of …

A Universal Framework for General Prediction of Physicochemical Properties: The Natural Growth Model

J Fan, C Qian, S Zhou - Research, 2024 - spj.science.org
To precisely and reasonably describe the contribution of interatomic and intermolecular
interactions to the physicochemical properties of complex systems, a chemical message …