多尺度感知图形嵌入方法揭示了LC-61,一种强大的抗Leishmania infantum化合物
Vinícius Alexandre Fiaia Costa1, Alexandra Maria Dos Santos Carvalho2, Rafael Consolin Chelucci3
1Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, 74605-170 Goiânia, Brazil.
Journal of chemical information and modeling
|March 17, 2026
概括
这项研究引入了一个图形神经网络 (GNN) 框架,用于发现针对内脏莱什曼病的新药. 通过GNN方法,成功地发现了一种强大的抗莱什曼素化合物LC-61,具有有利的特性,可用于进一步开发.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 寄生虫学的寄生虫学
背景情况:
- 由*Leishmania infantum*引起的内脏莱什曼病是一种致命的疾病,治疗选择有限.
- 加快发现新型抗莱什曼药物化合物的速度至关重要.
研究的目的:
- 开发和验证图形神经网络 (GNN) 框架,用于识别新型抗莱什曼病候选药物.
- 选一个大型化合物库,并确定有前途的化合物.
主要方法:
- 实施一个GNN框架,整合多层次机制,以提高预测性能.
- 在两个抗莱什曼病数据集上对GNN的评估,显示了显著的AUC改善.
- 对大约130万种化合物的选,以确定潜在的候选药物.
主要成果:
- 与默认GNN相比,GNN框架在预测准确度方面取得了显著的改进.
- 确定LC-61作为一种强大的抗莱什曼毒剂,具有纳米分子活性 (IC50=0.076μM) 和低细胞毒性 (CC50=157μM).
- LC-61在体外表现出有利的ADME特性,包括高溶解度和平衡的脂性.
结论:
- 该GNN框架有效地加速了新型抗莱什曼化合物的识别.
- LC-61是一种经过验证的命中化合物,具有在内脏莱什曼病药物发现中为命中到的优化提供有希望的特性.
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