Neonious is contracted to help to assess a drug for safety and adverse reactions.
Besides manual analysis, Neonious' research team utilizes computational pipelines.
These pipelines queue simulations (docking + MD) to be run by the Neonious Network, paid with MDSIMs.
Sombody running the Neonious software, thus being part of the Neonious Network, runs the simulations.
This person is rewarded with easy transferable MDSIMs, which he or her can exchange for real money.
Neonious GmbH was founded three years ago in 2018.
Neonious Biotech is actively driving drug research with its own pipelines and research team.
Our distributed network is a very cost-efficient option for us and others to run simulations.
The simulation results are verified by other computers in the Neonious Network.
The MDSIM token, key to pay for simulations fast and efficiently, is also a good investment opportunity.
We believe it is beneficial to reduce risks by contract us to analyse your drug, preferable before starting preclinical research, latest before phase 2 of the human trials.
For lower cost, we can utilize the Neonious Network. Alternatively, the calculations can also be done on more private computing resources.
At the core of our optimized pipeline, we perform docking simulations of a drug's active ingredient with most of the catalogized proteins of the Protein Data Bank archive in various conformations. To achieve these conformations, many MD simulations with the proteins were performed. In some cases, the docking simulation results are verified with additional MD simulations.
Our research team analyses the simulation results for safety and adverse reactions. Also, they run additional simulations or do lab tests where needed. 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.