QUANTUM-TOX

Revolutionizing Computational Toxicology

Industry: Quantum Chemistry

Country: UK, Portugal, Germany

Website: https://cordis.europa.eu/project/id/101130724

Toxicology stands at a pivotal juncture, driven by the increasing influx of drugs to the market and the expanding environmental footprint of various chemicals. There is a critical need for rapid, cost-effective, and precise technologies to evaluate toxic effects. Computational toxicology offers a range of tools and methodologies for predicting toxicity exclusively through computer-based approaches. In principle, computational toxicology holds significant advantages due to its swift and economical testing compared to in vitro methods. However, existing computational toxicology approaches suffer from significant limitations. Predictions predominantly rely on Quantitative Structure-Activity Relationship (QSAR) models, which are built on extensive sets of molecular descriptors. This poses challenges as these methodologies struggle to assess chemicals outside the scope of the QSAR models. Furthermore, the extensive number of descriptors hampers interpretability. Thus, there is a pressing need for novel methodologies to address these shortcomings.

 

This project aims to develop a novel descriptor, rooted entirely in quantum mechanics, capable of encompassing the entirety of chemical space while relying on a concise set of easily interpretable parameters. By initiating with meaningful chemical perturbations that elucidate the behavior of chemicals within assumed toxic action mechanisms, the approach will formulate specific Electronic SIGNatures (ESigns). These ESigns, mathematical invariants derived from quantum chemical calculations, will be correlated with toxicity using Artificial Intelligence. This pioneering approach heralds a significant paradigm shift in computational toxicology. It transcends limitations associated with molecular structure-based predictions, utilizes fewer parameters, and aligns with emerging trends in toxicology concerning the utilization of pathway information. Indeed, it represents a potent tool for precise toxicology predictions based solely on biochemical and chemical insights.

 

PARTICIPANTS

UNIVERSIDADE DA BEIRA INTERIOR

FASTCOMPCHEM, LDA

POSITIVAZIMUTE-LDA

BCNP CONSULTANTS GMBH

 

PARTNERS

THE UNIVERSITY OF MANCHESTER

LIVERPOOL JOHN MOORES UNIVERSITY