Ge Yichao, Yang Mengjie, Yu Xinyuan, Zhou Ying, Zhang Yintao, Mou Minjie, Chen Zhen, Sun Xiuna, Ni Feng, Fu Tingting, Liu Shuiping, Han Lianyi, Zhu Feng
College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China.
Nucleic Acids Res. 2025 Jan 6;53(D1):D1683-D1691. doi: 10.1093/nar/gkae868.
The measurement of cell-based molecular bioactivity (CMB) is critical for almost every step of drug development. With the booming application of AI in biomedicine, it is essential to have the CMB data to promote the learning of cell-based patterns for guiding modern drug discovery, but no database providing such information has been constructed yet. In this study, we introduce MolBiC, a knowledge base designed to describe valuable data on molecular bioactivity measured within a cellular context. MolBiC features 550 093 experimentally validated CMBs, encompassing 321 086 molecules and 2666 targets across 988 cell lines. Our MolBiC database is unique in describing the valuable data of CMB, which meets the critical demands for CMB-based big data promoting the learning of cell-based molecular/pharmaceutical pattern in drug discovery and development. MolBiC is now freely accessible without any login requirement at: https://idrblab.org/MolBiC/.
基于细胞的分子生物活性（CMB）测量对于药物研发的几乎每一个步骤都至关重要。随着人工智能在生物医学中的蓬勃应用，拥有CMB数据对于促进基于细胞模式的学习以指导现代药物发现至关重要，但尚未构建提供此类信息的数据库。在本研究中，我们引入了MolBiC，这是一个旨在描述在细胞环境中测量的分子生物活性的有价值数据的知识库。MolBiC具有550093个经过实验验证的CMB，涵盖988个细胞系中的321086个分子和2666个靶点。我们的MolBiC数据库在描述CMB的有价值数据方面独具特色，满足了基于CMB的大数据在药物发现和开发中促进基于细胞的分子/药物模式学习的关键需求。MolBiC现在无需登录即可免费访问：https://idrblab.org/MolBiC/ 。
