QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis

ABSTRACT

Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.

KEYWORDS:

• Macrolides
• QSAR
• machine learning
• structural interpretation
• Tetrahymena pyriformis
• acute toxicity

Acknowledgements

The authors thank Dr. Igor Tetko from the OCHEM Team (https://ochem.eu/) for use of the free software.

Additional information

Funding

Part of this work was supported by the budget of the IPAC RAS State Targets 2020 [topic № 0090-2019-0004].