计算机科学
构造(python库)
接口(物质)
分子动力学
相似性(几何)
药物发现
纳米技术
用户界面
人工智能
人机交互
分子模型
系列(地层学)
应用程序编程接口
系统工程
计算科学
软件工程
分子描述符
遗传程序设计
作者
Haihan Liu,Xiaoli Yan,Hao Fang,Hu Ge,Xuben Hou
标识
DOI:10.1021/acs.jcim.6c00014
摘要
Artificial intelligence (AI) has demonstrated remarkable potential in reshaping modern drug discovery, yet its widespread adoption is hindered by fragmented tools, high technical barriers, and the lack of user-friendly interfaces. Here, we present WeMol, an AI-driven one-stop molecular computing platform designed to streamline early-stage drug discovery. WeMol integrates a series of modules, covering molecular similarity search, structure-based and AI-enhanced docking, ADMET prediction, molecular generation, and molecular dynamics simulations. The platform features a zero-code, cloud-based interface that enables researchers without programming expertise to construct and execute comprehensive computational workflows. By integrating advanced AI algorithms with practical applications, WeMol lowers the entry barrier for nonexperts and provides a versatile, accessible, and reproducible solution to accelerate early drug design and discovery.
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