TOKSİKOLOJİDE MAKİNE ÖĞRENMESİ UYGULAMALARI: GÜNCEL YAKLAŞIMLAR VE GELECEK PERSPEKTIFLERİ

Abstract

Amaç: Toksikoloji, artan kimyasallar ve karmaşık maruziyet senaryolarıyla zorluklarla karşı karşıyadır. Bu derleme, 1960'lardaki QSAR modellerinden günümüzün derin öğrenme algoritmalarına kadar toksikolojide yapay zeka (AI) ve makine öğrenmesi (ML) uygulamalarının tarihsel gelişimini ve mevcut durumunu incelemektedir. İlaç keşfi, toksikokinetik, nanotoksikoloji ve çevresel  toksikoloji  gibi  alanlarda  AI  entegrasyonu  ile  kardiyovasküler  toksisite, hepatotoksisite, karsinogenez, genotoksisite ve nörotoksisite gibi toksikolojik sonlanım noktalarında sağlanan ilerlemeler değerlendirilmiştir.

Sonuç ve Tartışma: AI teknolojileri, hayvan deneylerinin azaltılması, hızlı örüntü tespiti ve minimum deneysel veriyle toksisite tahmini gibi önemli avantajlar sunmaktadır. Genomik, proteomik ve kimyasal yapı verilerinin entegre analizi, moleküler düzeyde toksisite mekanizmalarını aydınlatırken, çoklu organlar için geliştirilen ML modelleri ilaç kayıp oranlarının azaltılmasına katkıda bulunmaktadır. Veri kalitesi, model validasyonu ve yorumlanabilirlik, aşılması gereken başlıca zorluklar olarak kalmaktadır. Gelecekte, geleneksel toksikolojik yöntemlerin modern hesaplamalı yaklaşımlarla entegrasyonu, risk değerlendirmelerinde daha güvenilir ve verimli sonuçlar sağlarken, düzenleyici otoritelerin AI destekli modeller için standartlar geliştirmesi gerekmektedir. Multi-omik veri entegrasyonu, kişiselleştirilmiş toksikoloji yaklaşımları ve insanlarda toksisite mekanizmalarını gösterebilen yeni ML algoritmalarının geliştirilmesi, toksikoloji bilimlerinin ilerlemesi için umut verici yönleri temsil etmektedir.

Keywords

• AOP
• makine öğrenmesi
• PBTK
• QSAR
• toksikoloji

MACHINE LEARNING APPLICATIONS IN TOXICOLOGY: CURRENT APPROACHES AND FUTURE PERSPECTIVES

Abstract

Objective: Toxicology faces challenges with increasing chemicals and complex exposure scenarios. This review examines the historical development and current status of artificial intelligence (AI) and machine learning (ML) applications in toxicology, from QSAR models of the 1960s to today's deep learning algorithms. Advances achieved through AI integration in fields such as drug discovery, toxicokinetics, nanotoxicology, and environmental toxicology, along with toxicological endpoints including cardiovascular toxicity, hepatotoxicity, carcinogenesis, genotoxicity, and neurotoxicity, have been evaluated.

Result and Discussion: AI technologies offer significant advantages such as reduction in animal experimentation, rapid pattern detection, and toxicity prediction with minimal experimental data. Integrated analysis of genomic, proteomic, and chemical structure data elucidates toxicity mechanisms at the molecular level, while ML models developed for multiple organs contribute to reducing drug attrition rates. Data quality, model validation, and interpretability remain primary challenges to be overcome. In the future, integration of traditional toxicological methods with modern computational approaches will provide more reliable and efficient results in risk assessments, while regulatory authorities need to develop standards for AI-supported models. Multi-omic data integration, personalized toxicology approaches, and development of new ML algorithms that can illustrate toxicity mechanisms in humans represent promising directions for advancing the toxicology sciences. 

Keywords

• AOP
• machine learning
• PBTK
• QSAR
• toxicology

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