AI-driven · in silico · in vitro · human-relevant
PharmaToxAI integrates computational toxicology, ADMET modeling and human cellular models to support earlier, better-informed decisions on compounds and ingredients.
The problem
Computational models can screen large chemical spaces quickly, but confidence drops outside their domain. Experimental systems provide biological evidence, but cannot be applied indiscriminately to every candidate. PharmaToxAI is built around the feedback loop between them.
Solutions
Modular analyses can be used independently or combined into an integrated evaluation workflow.
Computational assessment of toxicity, ADMET and molecular behavior to characterize compounds before experimental testing.
Experimental evaluation in human cellular systems to test biological responses, refine computational hypotheses and generate decision-relevant evidence.
Integration of in silico predictions, read-across evidence and targeted in vitro results to prioritize compounds and support decisions before more resource-intensive development stages.
Workflow
Compound, ingredient, formulation or candidate is characterized together with the decision that needs to be made.
AI/ML, QSAR/QSPR, molecular modeling and PBPK/ADME methods estimate safety and biological behavior.
Selected endpoints are tested in human 2D/3D cellular systems where experimental confirmation adds value.
Computational and experimental evidence are interpreted together in a consolidated technical report.
Technology
The core pipeline uses established machine-learning approaches and rigorous validation, with deeper models or mechanistic simulations added when the endpoint justifies them.
Scientific validation
PharmaToxAI emphasizes validation strategy as strongly as algorithm selection: scaffold-aware splits, independent external validation and iterative comparison against experimental data are used to reduce chemical memorization and expose model limits.
Applicability matters. Predictions are interpreted in the context of the chemistry represented during training.
Experimental feedback matters. In selected endpoints, predictions can be tested in cells and used to refine subsequent modeling.
Decision context matters. Outputs are structured to support early prioritization rather than replace downstream evidence.
Applications
Candidate prioritization, safety profiling, ADMET characterization and preclinical decision support.
Ingredient safety, dermal compatibility and animal-reduction strategies supported by predictive and human-cell methods.
Custom computational and experimental studies for compounds, formulations and mechanistic questions.
Team
PharmaToxAI combines computational modeling with experimental physiology, cell biology and human-relevant models at IFIBIO Houssay (UBA–CONICET).
Computational modeling · bioinformatics · AI
Pharmacist and PhD in Pharmacy. Computational drug design, molecular dynamics, scientific data analysis and machine learning.
Cell physiology · aquaporins · experimental models
CONICET Adjunct Researcher. Cellular and molecular pathophysiology, renal physiology, cell migration and 2D/3D experimental systems.
Renal cell physiology · cancer biology · 3D models
CONICET Adjunct Researcher. Cellular physiology, acid-base regulation, aquaporins, renal cancer and experimental cell systems.
Placental biology · vascular development · human cell models
CONICET Adjunct Researcher. Molecular mechanisms of human placental vasculature development and its dysregulation in preeclampsia.
Contact
Tell us what decision you need to make and what data you already have. We can define the appropriate computational, experimental or integrated evaluation strategy.
IFIBIO Houssay · UBA–CONICET · Buenos Aires, Argentina