Szczegóły publikacji
Opis bibliograficzny
Towards rational pesticide design with graph machine learning models for ecotoxicology / Jakub ADAMCZYK // W: CIKM '25 [Dokument elektroniczny] : proceedings of the 34th ACM International Conference on Information and Knowledge Management : November 10-14, 2025, Seoul, Republic of Korea / Association for Computing Machinery. — Wesja do Windows. — Dane tekstowe. — New York, United States : Association for Computing Machinery, 2025. — e-ISBN: 979-8-4007-2040-6. — S. 6777-6780. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 6780, Abstr. — Publikacja dostępna online od: 2025-11-10
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165763 |
|---|---|
| Data dodania do BaDAP | 2026-01-30 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3746252.3761660 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | ACM International Conference on Information and Knowledge Management 2025 |
Abstract
This research focuses on rational pesticide design, using graph machine learning to accelerate the development of safer, eco-friendly agrochemicals, inspired by in silico methods in drug discovery. With an emphasis on ecotoxicology, the initial contributions include the creation of ApisTox, the largest curated dataset on pesticide toxicity to honey bees. We conducted a broad evaluation of machine learning (ML) models for molecular graph classification, including molecular fingerprints, graph kernels, GNNs, and pretrained transformers. The results show that methods successful in medicinal chemistry often fail to generalize to agrochemicals, underscoring the need for domain-specific models and benchmarks. Future work will focus on developing a comprehensive benchmarking suite and designing ML models tailored to the unique challenges of pesticide discovery.
