惰性DB作为来自PubChem的生物不活跃小分子的生成AI扩展资源
Seungchan An1, Yeonjin Lee1, Junpyo Gong1
1College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul, 08826, Republic of Korea.
Journal of cheminformatics
|April 11, 2025
概括
InertDB为人工智能 (AI) 药物发现提供了非活性化合物的关键数据集. 这个资源通过解决化学预测中缺少负面数据的问题来提高AI模型的准确性.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 药理学中的人工智能
背景情况:
- 强大的人工智能 (AI) 模型需要高质量,多样化的化学数据集.
- 负面数据的稀缺性和对积极结果的出版偏差阻碍了准确的生物活动预测.
研究的目的:
- 介绍InertDB,这是一个由精选无活性化合物 (CIC) 和生成无活性化合物 (GIC) 组成的全面数据库.
- 解决AI驱动的药理学预测建模不足负面数据的挑战.
主要方法:
- 从超过460万个PubChem记录中精选了3205种无活性化合物 (CIC).
- 使用自然语言处理 (NLP) 聚类指标进行CIC选择的优先级生物试验多样性.
- 使用在CIC上训练的深度生成AI模型生成了64,368种非活性化合物 (GIC).
主要成果:
- 97.2%的CIC坚持五项规则,超过了PubChem数据集的平均值.
- InertDB显著提高了AI预测性能,特别是在表型活动预测方面.
- 与随机抽样和诱生成方法相比,证明了更高的性能.
结论:
- 通过提供可靠的非活性化合物数据集,InertDB填补了人工智能驱动的药物发现的关键缺口.
- 利用NLP和生成AI来创建一个有价值的资源,以提高预测准确性和模型可靠性.
- 为制药学研究中生成培训数据的传统方法提供了改进的替代方案.
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