堆积器:一种基于SMILES的新堆积方法,用于加速和有效地发现ERα和ERβ抗剂
Nalini Schaduangrat1, Nutta Homdee1, Watshara Shoombuatong2
1Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
Scientific reports
|December 27, 2023
概括
我们开发了StackER,这是一种机器学习方法,用于识别雌激素受体 (ER) 抑制剂用于乳腺癌治疗. StackER提高了预测准确度,有助于药物发现和重新定位努力.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
- 乳腺癌研究研究 乳腺癌研究
背景情况:
- 雌激素受体 (ERs) 在乳腺癌中至关重要,但治疗耐药性影响了许多患者.
- 针对ER阳性乳腺癌的现有治疗方法面临着由于先天性和获得性耐药性的挑战.
- 机器学习 (ML) 为治疗化合物的大规模识别提供了具有成本效益的解决方案.
研究的目的:
- 开发一种有效和加速的方法来识别ERα和ERβ抑制剂.
- 为了创建一个名为StackER的新型堆叠机器学习模型.
- 通过改进抑制剂识别来解决乳腺癌治疗耐药性的问题.
主要方法:
- 建立了ERα (1,996个化合物) 和ERβ (1,207个化合物) 的最新数据集.
- 使用基于SMILES的特征描述器和ML算法来生成概率特征 (PF).
- 开发了一个高效的堆叠模型,使用两步特征选择策略.
主要成果:
- 在预测ERα和ERβ抑制剂方面,StackER比传统的ML分类器和现有方法表现优越.
- 达到了高的马修斯相关系数 (MCC) 值:0.829-0.847 (交叉验证) 和0.712-0.786 (独立测试).
- 确定了ER抑制的关键特征和潜在的FDA批准的ERα抑制候选药物.
结论:
- StackER提供了一种创新的,高效的堆叠方法来识别ER抑制剂.
- 这种方法促进了药物的重新用途,并缩小了对潜在乳腺癌治疗药物的查.
- 预计StackER将推动整个社区在ER抑制剂发现方面的努力.
相关概念视频
01:28Structure-Activity Relationships and Drug Design
726
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
726
01:16Adrenergic Agonists: Chemistry and Structure-Activity Relationship
3.0K
Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
3.0K