Project Overview

Today, most chemicals are still tested in animals or not tested at all. Computational toxicology promised a better path, but existing models only work reliably for molecules similar to ones already in the database. Step outside that zone, and predictions become guesswork.

Quantum-Tox wants to change that. Funded by the European Innovation Council, our project is developing a fundamentally new class of molecular descriptors – Electronic Signatures (E-Signs) – grounded in quantum mechanics. For the first time, this makes it possible to assess molecules across the entire chemical space, not just the corner of it we already know.

Electronic Signatures (E-Signs) are compact, quantum-mechanics-based descriptors that capture a molecule’s

behaviour from the inside (from its electron density and quantum state) rather than from external structural

features. By combining E-Signs with AI-driven models, Quantum-Tox can make toxicity predictions that are both

more accurate and more interpretable than anything currently available.

Alongside the science, Quantum-Tox is training and empowering a new generation of researchers, bringing

together early-career scientists in quantum chemistry, artificial intelligence, toxicology, and regulatory science,

and giving them the tools and collaborative environment to build something genuinely new.

Project Objectives

Quantum-Tox is structured around four concrete objectives, each one building on the last, from fundamental quantum chemistry through to practical software tools ready for industry and regulators:

1

to develop the first generation of ESigns targeting computational toxicology predictions.

2

to develop an AI system for toxicity prediction using the ESigns.

3

to create a software engine, codifying the lessons derived from the previous objectives to make the results available for further extended use.

4

to train and test the AI models using the ESigns for two liver conditions: steatosis and fibrosis.

Why we're excited

Expected Outcomes and Impact

The Quantum-Tox project will open new pathways in computational toxicology, not by improving existing methods, but by departing from them entirely. The result will be a new generation of models that are more accurate, more interpretable, and capable of covering the whole chemical space.

Concretely, Quantum-Tox will contribute to reducing animal testing in regulatory and drug discovery contexts, give regulators and industry transparent, defensible computational evidence, and accelerate the discovery of safer drugs and safer chemicals.

Microscope - Robot

To have enhanced
interpretability:

The use of meaningful chemical information will have a dramatic heuristic value, providing interpretability, confidence, and potentiating exploitation of the chemical knowledge. The studies will progressively move away from the empirical association between opaque parameters, entering the phase of use of explicit chemical knowledge to create a software engine, codifying the lessons derived from the previous steps, to make the results available for further, extended use. We will generate new models implementing the tools developed, and this will also provide a way to optimize and refine the software parameters used in the models for the toxicological endpoints.

To be able to investigate the entire chemical space:

The QUANTUM-TOX technology with the novel concept of electronic signatures (ESigns) as property descriptors can investigate the entire chemical space that otherwise is not possible.

Impacts

The new system based on quantum chemistry represents a massive departure from existing approaches that depend on a limited number of compounds with reliable experimental data, being able to cover the whole chemical space. The QUANTUM-TOX project will have major scientific/technological, societal and economic impacts:

Impact

It will provide effective science-based safety and risk assessments that will increase and improve the screening of human and animal drugs, food products, and environmental pollutants.

Impact

It will be pivotal to accelerate the transition to innovation without the use of animals in research, regulatory testing, and education.

Impact

It will disrupt the toxicology market and enable a technology shift from benchbased toxicology to computer-based assessment.

Impact

It will decrease the attrition rates in drug discovery, enabling new drugs to market faster and affordable

Impact

It will support the new approaches in toxicology (AOPs, NAMs, etc)

Impact

It will directly affect cheminformatics through new fast algorithms that can describe large systems, thus offering a replacement for the limited methods of today.