3 papers
cs.LG2025
MolPILE -- large-scale, diverse dataset for molecular representation learning
Jakub Adamczyk, Jakub Poziemski, Franciszek Job +2
The size, diversity, and quality of pretraining datasets critically determine the generalization ability of foundation models. Despite their growing importance in chemoinformatics,…
cs.LG2025
Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology
Jakub Adamczyk
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 i…
cs.LG2025
Evaluating machine learning models for predicting pesticide toxicity to honey bees
Jakub Adamczyk, Jakub Poziemski, Pawel Siedlecki
Small molecules play a critical role in the biomedical, environmental, and agrochemical domains, each with distinct physicochemical requirements and success criteria. Although biom…