Research
Research Interests
My research lies at the intersection of computational chemistry, structural biology, and drug discovery. I develop and apply computational methods — including structure-based drug design, docking, molecular dynamics simulations, and active learning — to identify and optimize small-molecule therapeutics against a broad range of disease targets. A parallel focus of my work is the development of open-source scientific software to make these methods more accessible and interoperable within the research community.
Keywords: drug discovery · Computational chemistry · Structure-based drug design · Molecular dynamics simulations · Molecular docking · Virtual screening · Active learning · Chemical space exploration · Scientific software development
Funded Research Projects
Center for Antiviral Medicines and Pandemic Preparedness (CAMPP)
2023 – Present | Funder: NIAID-NIH AViDD Center | Role: Computational Scientist
https://www.campp.org
CAMPP is one of nine NIH-funded Antiviral Drug Discovery (AViDD) Centers for Pathogens of Pandemic Concern, with a mission to develop broad-spectrum antiviral drugs against coronaviruses and other viruses with pandemic potential. As part of Core E — Structural Biology and Computational Modeling, I provided computational modeling support during the hit identification and lead optimization phases, applying structure-based techniques to prioritize candidates for experimental testing.
Computer-Aided Drug Discovery National Resource — AutoDock Suite
2023 – present | Funder: NIGMS-NIH | Role: Computational Scientist | PI: Dr. Stefano Forli, The Scripps Research Institute | Scripps News
This $5.2M, five-year NIH/NIGMS-funded initiative supports the maintenance and modernization of the AutoDock Suite software — the most widely used molecular docking platform globally. The project will help establish a national resource that makes advanced computational docking methods accessible to a wide community of researchers, while adapting the suite to evolving hardware platforms and operating systems, incorporating the latest algorithmic developments, and supporting its large user community. As a member of the Forli lab, I contribute to software development and validation across multiple components of the suite.
Related publications: [17], [20], [21], [23]
REFRACT — Repeat Protein Function Refinement, Annotation and Classification of Topologies
2019 – 2020 | Funder: Marie Skłodowska-Curie Action-RISE (H2020) | Role: Research Exchange Fellow | Coordinator: Dr. Silvio Tosatto, Università degli Studi di Padova, Italy | https://refract-rise.eu
REFRACT is a European H2020 international research consortium focused on the structural and functional characterization of tandem repeat proteins. I completed a six-month research exchange at the Laboratoire de Bioinformatique Structurale et Modélisation Moléculaire (BiSMM), CNRS, Montpellier, France, under the supervision of Dr. Andrey Kajava, contributing computational structural analyses of repeat protein conformations and amyloid fibril structure.
Related publications: [1]
Target Validation and AI-Guided Identification of T. cruzi Phosphodiesterase Inhibitors
2020 – 2022 | Funder: Global Health Innovative Technology (GHIT) Fund | Role: Computational Scientist | Coordinator: Eisai Co., Ltd., Japan | https://www.ghitfund.org
This multicentric project, coordinated by the Japanese pharmaceutical company Eisai and funded by the GHIT Fund, aimed to validate Trypanosoma cruzi phosphodiesterases (PDEs) as drug targets for Chagas disease and identify selective inhibitors using a computationally enhanced screening cascade. Chagas disease affects approximately 7 million people in the Americas and remains severely undertreated. I provided in silico modeling support during the hit identification phase, combining ligand- and structure-based techniques.
Related publications: [11]
Drug Discovery Against SARS-CoV-2 Major Protease (DaMPro-CoV-2)
2020 – 2022 | Funder: Institut Pasteur International Network | Role: Computational Scientist | Coordinator: Dr. Marcelo Comini, Institut Pasteur Montevideo, Uruguay
DaMPro-CoV-2 was a multicentric, multidisciplinary international drug discovery project aimed at identifying and characterizing inhibitors of the SARS-CoV-2 cysteine-dependent viral proteases, particularly the Main Protease (Mpro). The consortium brought together over 30 research groups across Latin America and Europe. I provided ligand- and structure-based computational modeling support during the hit identification stage.
Related publications: [5], [6], [14]
pH and Cancer: Pharmacological Modulation of the Hv1 Proton Channel as a Therapeutic Strategy in Mammary Carcinogenesis
2022 – 2025 | Funder: PICT-2020-SERIEA-02189 (ANPCyT) | Role: Computational Scientist | Coordinator: Dra. Clara Ventura, National University of La Plata
This project investigated the human voltage-gated proton channel (Hv1) as a pharmacological target in mammary carcinogenesis. I performed computational studies to identify and characterize an intracellular ATP-binding site that activates the channel.
Related publications: [12]
Computer-Assisted Drug Repurposing for Infectious Tropical and Neglected Diseases (Chagas, Echinococcosis, Toxoplasmosis, Malaria)
2017 – 2022 | Funder: CONICET/ANPCyT | Role: Computational Scientist | Coordinator: Prof. Dr. Alan Talevi | LIDeB
This research line applied computational drug repurposing strategies to identify therapeutic candidates for infectious neglected tropical diseases (NTDs) with limited or no pharmacological treatment options. My work encompassed structure- and ligand-based drug discovery applied across multiple parasitic targets, including T. cruzi carbonic anhydrase (TcCA) and T. cruzi polyamine transporter (TcPAT12).
Related publications: [3], [10], [18]
Rational Discovery of Therapeutics for Refractory Epilepsy
2017 – 2022 | Funder: CONICET/ANPCyT | Role: Computational Scientist | Coordinator: Prof. Dr. Alan Talevi | LIDeB
This project, which formed the basis of my doctoral thesis, sought to discover more efficacious and better-tolerated drugs for the treatment of refractory epilepsy — a condition in which approximately 30% of patients fail to achieve seizure control with available pharmacotherapies. I applied ligand- and structure-based techniques to identify hits acting on TRPV1 and NaV ion channels.