Research progress of artificial intelligence-based small molecule generation models in drug discovery

VernacularTitle:基于人工智能的小分子生成模型在药物发现中的研究进展
Author: qian TANG ¹ ; Roufen CHEN ; Zheyuan SHEN ; Xinglong CHI ; Jinxin CHE ; Xiaowu DONG
Author Information

1. 浙江省药品化妆品审评中心
Publication Type:Journal Article
Keywords: small molecule generation model / drug discovery / artificial intelligence technology
From: Journal of China Pharmaceutical University 2024;55(3):295-305
CountryChina
Language:Chinese
Abstract: Abstract: With the rapid development of artificial intelligence technology, small molecule generation models have emerged as a significant research direction in the field of drug discovery. These models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have proven to possess remarkable capabilities in optimizing drug properties and generating complex molecular structures. This article comprehensively analyzes the application of the aforementioned advanced technologies in the drug discovery process, demonstrating how they supplement and enhance traditional drug design methods. At the same time, it addresses the challenges facing current methods in terms of data quality, model complexity, computational cost, and generalization ability, with a prospect of future research directions.