We are pioneers of in silico prediction of drug safety and adverse reactions, limiting cost risks and reducing harm done to animals and humans.

To have the best tools for our drug analysis, we constantly optimize our algorithms and pipelines, with an emphasis on machine learning (AI) algorithms and models.

The company behind Ambrego was founded
three years ago in 2018.

Ambrego is actively driving drug research
with its own pipelines and research team.

We have access to a very cost-efficient
computing network to run simulations.

We believe it is beneficial to reduce risks by contracting us to analyse your drug, preferable before starting preclinical research, latest before phase 2 of the human trials. If you have a drug which failed a trial because of adverse reactions, we can help to find the problem and to fix the drug.

At the core of our optimized pipeline, we perform docking simulations of a drug's API with proteins of the Protein Data Bank archive. We utilize machine learning (AI) models to efficiently find the right proteins to concentrate on. The docking simulation is done without the need to bring the protein or the API into specific conformations, again with help of machine learning models.

With the help of heuristics and AI, our research team analyses the simulation results for safety and adverse reactions. Also, they run additional simulations or do lab tests where needed, based on pre-defined processes. At the end of the process, a detailed report is written and presented to the customer.

Our approach can visualize the reason for adverse reactions, allowing molecules to be optimized for less adverse reactions instead of simply being discarded. Promising molecules which were already discarded because of adverse reactions we can also analyse, to potentially put them back into the process.

We can identify which preclinical studies and human trials will likely fail before they start. Besides reducing the cost risk, this reduces the total amount of morally questionable animal testing and results in less harmed humans in the trials. In some cases we find risks which stay undetected in the trials.