Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.
This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.
Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky Prospect, 31a, 119071, Moscow, Russia, nkireeva@gmail.com.
Natalia V
NV
Kireeva
Svetlana I
SI
Ovchinnikova
Sergey L
SL
Kuznetsov
Andrey M
AM
Kazennov
Aslan Yu
AY
Tsivadze
D001185:Artificial Intelligence; D015197:Carcinogenicity Tests; D016000:Cluster Analysis; D003198:Computer Simulation; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors; D065427:Factor Xa Inhibitors; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012984:Software; D013329:Structure-Activity Relationship
D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; D065427:Factor Xa Inhibitors