5 papers
Few-Shot Bioactivity Prediction with Meta-Learning under Assay Heterogeneity
Michal Kmicikiewicz, Tommy Rochussen, Vincent Fortuin +1
Accurate bioactivity prediction is a central challenge in early-stage drug discovery, as individual assays often contain too few measurements to train reliable models independently…
Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
Serra Korkmaz, Adam Izdebski, Jonathan Pirnay +5
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yiel…
seqme: a Python library for evaluating biological sequence design
Rasmus Møller-Larsen, Adam Izdebski, Jan Olszewski +4
Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity o…
PepCompass: Navigating peptide embedding spaces using Riemannian Geometry
Marcin Możejko, Adam Bielecki, Jurand Prądzyński +10
Antimicrobial peptide discovery is challenged by the astronomical size of peptide space and the relative scarcity of active peptides. Generative models provide continuous latent "m…
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek
Designing protein sequences of both high fitness and novelty is a challenging task in data-efficient protein engineering. Exploration beyond wild-type neighborhoods often leads to…