发现了血管内皮生长因子受体2抑制剂的发现,使用结木变异自编码器与贝叶斯优化和梯度上升的贝叶斯优化
Gia-Bao Truong1, Thanh-An Pham1, Van-Thinh To1
1Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.
ACS omega
|December 2, 2024
概括
研究人员利用深度学习发现了抑制血管内皮生长因子受体2 (VEGFR-2) 的新型小分子,这是抗癌药物开发的关键标. 这种方法确定了未来癌症治疗的有希望的候选人.
科学领域:
- 药用化学 医学化学
- 计算机化药物发现技术
- 在瘤学瘤学.
背景情况:
- 血管内皮生长因子受体2 (VEGFR-2) 是开发新抗癌药物的关键标.
- 蛋白质氨酸激酶家族成员VEGFR-2是瘤血管生成的核心.
- 持续需要新的小分子来有效抑制VEGFR-2活性.
研究的目的:
- 通过深度学习探索化学空间,寻找抑制VEGFR-2的新型小分子.
- 为了确定针对VEGFR-2的潜在抗癌药物候选者.
主要方法:
- 使用了一个结树变异自编码器,与潜空间优化 (贝叶斯优化和梯度上升) 相结合.
- 产生了493种新的小分子.
- 使用定量结构-活性关系 (QSAR) 模型和与4ASD复合体相对应的分子对接来评估抑制潜力.
主要成果:
- QSAR模型显示出高的预测准确性 (内部验证的R2为0.792±0.075,外部验证的R2为0.859).
- 分子对接显示出有希望的结果 (ROC-AUC为0.710,结合亲和度值为-7.90 kcal/mol).
- 分析了入围的分子与关键残留物 (Cys919,Asp1046,Glu885) 的结合相互作用.
结论:
- 深度学习方法成功生成了具有潜在VEGFR-2抑制活性的新型小分子.
- 这些已识别的分子代表了开发下一代抗癌疗法的有希望的线索.
- 这些候选药物的进一步验证和优化对于临床开发是有必要的.
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