Modeling how physical forces shape living systems
Featured Publications
SIMBA-GNN: mechanistic graph learning for microbiome prediction
Predicting how gut microbial communities assemble and change requires models that capture the underlying mechanisms driving interspecies interactions, not just taxonomic correlations. We present SIMBA, a simulation-augmented graph neural...
Semi-supervised Retrieval of Functional Residues Through the Integration of Protein Language Models and Gene Ontology Data
Abstract Motivation Experimental studies of protein function often focus on mechanistic descriptions, characterizing how specific sites and residues contribute to activity. Abstractions such as domains and active sites enable quantitativ...
Expanding the HP1a-binding consensus and molecular grammar for heterochromatin assembly
The recruitment of Heterochromatin Protein 1 (HP1) partners is essential for heterochromatin assembly and function, yet our knowledge regarding their organization in heterochromatin remains limited. Here, we show that interactors engage ...
Computational drug design in the artificial intelligence era: A systematic review of molecular representations, generative architectures, and performance assessment
Generative drug design has emerged as a transformative approach in pharmaceutical research, leveraging deep learning models to create novel molecules with targeted properties. This systematic review analyzes the current landscape of comp...
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