7 citations · 23 across the 8 of their papers we have counts for
8 papers
MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery
Hongxuan Liu, Roman Bushuiev, Ivy Lightheart +12
Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery. Subtle issues in experimental design and mod…
De novo generation of functional terpene synthases using TpsGPT
Hamsini Ramanathan, Roman Bushuiev, Matouš Soldát +5
Terpene synthases (TPS) are a key family of enzymes responsible for generating the diverse terpene scaffolds that underpin many natural products, including front-line anticancer dr…
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…
One protein is all you need
Anton Bushuiev, Roman Bushuiev, Olga Pimenova +9
Generalization beyond training data remains a central challenge in machine learning for biology. A common way to enhance generalization is self-supervised pre-training on large dat…
MassSpecGym: A benchmark for the discovery and identification of molecules
Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge +27
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is…
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…