We provide interpretable predictions for drug discovery
AI-enhanced systems biology enables interpretable predictions for the right decisions for successful drug development. The more interpretable information ensures the less risk.
We collect data constantly and link them in meaningful ways. Networks connected in a meaningful way have more potential than the original data. In particular, we have developed a simulation technique that reflects the causal nature of biological networks, which is useful for understanding and avoiding the risk of drug discovery.
The process of developing a new drug consists of a number of decision-making processes. NetTargets' interpretable prediction helps us make better decisions. Computer-produced interpretable predictions have their correctness to be assessed by humans, giving them the opportunity to reasonably choose their strategy, helping human-to-human and human-to-machine cooperation.
Only less than 10% of clinical trials succeed. 40% of them fail because they could not find a relationship between drug and disease, and 30% fail by testing the wrong patient group. Although failure is unavoidable, NetTarget's mechanism-based treatment strategy analysis technology can significantly reduce the risk of failure of drugs entering clinical trials.
Systems biology integrates multi-diciplinary technologies and revolutionizes drug discovery by converting black boxes into white boxes.
Mathematical modeling is the process of replacing the problem of biology with the problem of engineering. To get a sophisticated mathematical model, we try to integrate as much data as possible.
With a computer simulation approach, we can explore a myriad of potential treatment strategies in a vast space, beyond the limitations of methods that rely solely on experiments.
Ultimately, the NetTargets' approach allows us to understand what's behind complex diseases, and further to gain deep insight into what strategies are appropriate for the treatment of diseases.