药效团
对接(动物)
愤怒(情绪)
计算机科学
分类器(UML)
机器学习
糖基化
虚拟筛选
计算生物学
药物发现
人工智能
化学
生物信息学
受体
生物
生物化学
医学
神经科学
护理部
作者
David Huang,Valentina L. Kouznetsova,Igor F. Tsigelny
出处
期刊:Physical Biology
[IOP Publishing]
日期:2020-03-16
卷期号:17 (3): 036003-036003
被引量:11
标识
DOI:10.1088/1478-3975/ab6819
摘要
The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, multiple hypothesized binding modes, and small amounts of activity data. The main objective of this study was to demonstrate the efficacy of deep-learning (DL) techniques on small bioactivity datasets, and to identify candidate inhibitors of RAGE. We applied transfer learning in the form of a semi-supervised molecular representation in order to address small dataset problems. To validate the candidate inhibitors, we examined them using more computationally expensive pharmacophore-modeling and docking techniques. We created a strong classifier of RAGE activity, producing 79 candidate inhibitors. These candidates agreed with docking models and were shown to have no significant statistical difference from pharmacophore-based results. The transfer-learning techniques used allow DL to generalize chemical features from small bioactivity datasets to a broader library of compounds with high accuracy. Furthermore, the DL model is able to handle multiple binding modes without explicit instructions. Our results demonstrate the potential of a broad family of DL techniques on bioactivity predictions.
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