韩国首次使用人类和小鼠肝脏微体稳定性数据进行药物发现挑战
Nam-Chul Cho1,2, SeongEun Hong3, Jin Sook Song4
1Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea. nccho@krict.re.kr.
Journal of cheminformatics
|September 3, 2025
概括
跳跃人工智能药物发现挑战 (JUMP AI 2023) 利用代谢稳定性数据促进韩国人工智能驱动的药物发现. 图形神经网络 (GNN) 方法在预测化合物稳定性方面显著成功.
科学领域:
- 药物发现
- 人工智能
- 计算化学
背景情况:
- 韩国化学银行 (KCB) 用人和小鼠肝脏显微体测试了大约4000种化合物的代谢稳定性数据集.
- 代谢稳定性是药物开发的关键因素,影响药物动力学和疗效.
- 预测代谢稳定性有助于早期候选药物选择.
研究的目的:
- 通过竞争性挑战在韩国促进人工智能驱动的药物发现.
- 鼓励开发用于预测化合物代谢稳定的新算法.
- 促进制药研究中的合作和创新.
主要方法:
- 药物发现跳跃人工智能挑战 (JUMP AI 2023) 数据集包括3,498个培训和483个测试化合物.
- 参与者开发了算法,以估计30分钟化后的化合物的剩余百分比.
- 使用了机器学习和深度学习模型,包括图形神经网络 (GNN).
主要成果:
- 1,254个团队参与,开发各种用于代谢稳定性的AI算法.
- 五个获奖团队成功使用了基于GNN的方法.
- 这项竞赛强调了人工智能在加速药物发现方面的潜力.
结论:
- 跳跃人工智能2023成功刺激了韩国药物发现中的人工智能应用.
- 在预测代谢稳定性方面,GNN显示出显著的前景.
- 这一举措对于推动该地区人工智能制药研究至关重要.
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