Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental …
Accurately predicting protein fitness with minimal experimental data is a persistent challenge in protein engineering. We introduce PRIMO (PRotein In-context Mutation Oracle), a …
Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, …
Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab …
Multiple Sequence Alignments (MSAs) are crucial in protein sequence analysis for identifying homologous proteins sharing a common evolutionary origin. However, traditional MSA …
Predicting molecular docking poses with flow matching algorithms represents both a promising opportunity and a challenging task. Recently, a flow matching algorithm, HarmonicFlow, …
With the growing demand for antibody therapeutics, there is a great need for computational methods to accelerate antibody discovery and optimization. Advances in machine learning …
We introduce M²Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially …