6 citations · 14 across the 5 of their papers we have counts for
5 papers
Generalization Beyond Benchmarks: Evaluating Learnable Protein-Ligand Scoring Functions on Unseen Targets
Jakub Kopko, David Graber, Saltuk Mustafa Eyrilmez +4
As machine learning becomes increasingly central to molecular design, it is vital to ensure the reliability of learnable protein-ligand scoring functions on novel protein targets.…
Learning to engineer protein flexibility
Petr Kouba, Joan Planas-Iglesias, Jiri Damborsky +3
Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus…
DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology
Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis +17
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML resea…
Revealing data leakage in protein interaction benchmarks
Anton Bushuiev, Roman Bushuiev, Jiri Sedlar +4
In recent years, there has been remarkable progress in machine learning for protein-protein interactions. However, prior work has predominantly focused on improving learning algori…
Learning to design protein-protein interactions with enhanced generalization
Anton Bushuiev, Roman Bushuiev, Petr Kouba +8
Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning appro…