Research Area - Bio- and Cheminformatics

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Biological and chemical systems are inherently complex, high-dimensional, and structured. They involve multiple interacting components — genes, proteins, molecules — that operate across scales, from molecular to systemic. Understanding these systems and predicting their behavior requires computational models that can effectively represent, integrate, and reason over heterogeneous data.

Bioinformatics and cheminformatics aim to transform biological sequences, molecular structures, and experimental data into actionable knowledge. However, modeling these domains remains challenging: data is often noisy, incomplete, or highly contextual; relevant signals are sparse and embedded in complex relational structures. From an informatics perspective, this demands the development of novel representations, robust learning methods, and explainable models that can bridge symbolic reasoning with statistical learning.

We currently collaborate with the Biochemtics research group at ICIC-CONICET, focusing on computational methods for molecular modeling and bioactivity prediction.

Current research interests in our research group:

• Learning graph-based representations of molecules and polymers.
• Modeling chemical structure–activity relationships (QSAR) with interpretable ML.
• Designing new XAI (eXplainable AI) methods for predictions.
• Integrating symbolic and statistical reasoning in molecular modeling.