In silico evaluation of toxic effects of benalaxyl-m on bees

Abstract

This study evaluated the acute toxic effects of Benalaxyl-M on honey bees (Apis mellifera) using BeeToxAI, a machine learning-based QSAR (Quantitative Structure-Activity Relationship) tool, through in silico methods. The analysis results indicated that Benalaxyl-M is non-toxic in terms of acute oral and contact toxicity for honey bees (83-86% reliability). The Applicability Domain analysis confirmed the reliability of the predictions, while contribution maps identified functional groups contributing to toxicity. Although the findings suggest a low acute risk of Benalaxyl-M to honey bees, experimental validation is recommended. This study highlights the potential of AI-based tools in the ecotoxicological risk assessment of pesticides.

Keywords

• Benalaxyl-M
• honey bees
• ecotoxicology
• in silico modeling
• QSAR
• BeeToxAI
• pesticide risk assessment

Benalaxyl-M Maddesinin Arılar Üzerindeki Toksik Etkilerinin In Silico Değerlendirilmesi

Abstract

Bu çalışmada, Benalaxyl-M maddesinin bal arıları (Apis mellifera) üzerindeki akut toksik etkileri, makine öğrenimi tabanlı bir QSAR (Kantitatif Yapı-Aktivite İlişkisi) aracı olan BeeToxAI kullanılarak in silico yöntemlerle değerlendirilmiştir. Analiz sonuçları, Benalaxyl-M'nin bal arıları için akut oral ve kontakt toksisite açısından toksik olmadığını (%83-86 güvenilirlik) göstermiştir. Uygulanabilirlik Alanı analizi, tahminlerin güvenilir olduğunu doğrularken, katkı haritaları moleküldeki toksisiteye katkı yapan fonksiyonel grupları belirlemede kullanılmıştır. Elde edilen bulgular, Benalaxyl-M'nin bal arıları üzerindeki düşük akut riskini ortaya koysa da, sonuçların deneysel çalışmalarla desteklenmesi önerilmektedir. Bu çalışma, pestisitlerin ekotoksikolojik risk değerlendirmelerinde yapay zeka tabanlı araçların potansiyelini vurgulamaktadır.

Keywords

• Benalaxyl-M
• bal arıları
• ekotoksikoloji
• in silico modelleme
• QSAR
• BeeToxAI
• pestisit risk değerlendirmesi

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