应用原子神经网络来偏向向生物活性类型构造的符合组合
Benoit Baillif1, Jason Cole2, Ilenia Giangreco2,3
1Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK.
Journal of cheminformatics
|December 22, 2023
概括
原子神经网络 (AtNN) 现在可以为虚拟选优先考虑生物活性小分子构造. 这种方法通过专注于可能的目标结构来提高药物发现的效率,优于传统的基于能源的排名.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 结构生物学 结构生物学
背景情况:
- 识别生物活性构成对于基于结构的虚拟查至关重要.
- 当前的方法很难从生成的集合中优先考虑最相关的构造.
- 现有的方法缺乏一般的方法来排序可能的目标绑定结构.
研究的目的:
- 开发和评估原子神经网络 (AtNN),用于预测小分子的生物活性构造.
- 评估AtNNs在排列组合中丰富生物活性类型构造的能力.
- 将AtNN的表现与已建立的生物活性无意识和基于生物活性的排名方法进行比较.
主要方法:
- 在PDBbind联结体的3D适应器数据上训练AtNN.
- 预测的原子根平均平方偏差 (ARMSD) 到最接近的生物活性构造.
- 使用BEDROC分数评估生物活性构成的早期丰富,并与Sage能量和TFD2SimRefMCS基线进行比较.
- 在刚性带重新对接和药探测试实验中测试了性能.
主要成果:
- 一个AtNN的ComENet获得了0.29 ± 0.02的BEDROC中位数,超过了Sage的能量排名 (0.18 ± 0.02) 并匹配了TFD2SimRefMCS (0.31 ± 0.02).
- 在AtNNs上,柔性分子显著改善 (基线BEDROC中位数为0.09-0.13对比至0.02).
- 与基线相比,ComENet排名的符合者改善了成功对接率 (0.48 ± 0.02) 和药物对冲率.
结论:
- 在分子组合中,AtNN有效地优先考虑生物活性类型的构造.
- 这种方法为虚拟选应用提供了显著的计算优势.
- 该方法通过专注于相关的形状,提高了识别潜在药物候选者的效率.
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