7 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
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,…
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…
Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
Mateusz Praski, Jakub Adamczyk, Wojciech Czech
Pretrained neural networks have attracted significant interest in chemistry and small molecule drug design. Embeddings from these models are widely used for molecular property pred…
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…
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
Jakub Adamczyk, Piotr Ludynia, Wojciech Czech
Understanding peptide properties is often assumed to require modeling long-range molecular interactions, motivating the use of complex graph neural networks and pretrained transfor…