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Far more efficient experiments
Simulation reduces the number of trials and the risk of failure, so bench time goes to the checks that matter. Automating data analysis also makes the work more efficient.
STORY
How BaraPhaSilico began
During my PhD I was a wet-lab researcher. The more time I spent at the bench, the more I felt how long it takes to get even a single result.
Around me, others were struggling too, waiting for results that would not come. It was not a lack of effort. Experiments test hypotheses one by one, and that simply takes time.
After graduating I stepped away from drug discovery for a time, but the wish to keep working on it did not change. I taught myself in silico methods and used the computer to narrow hypotheses. With the help of my collaborators, I was able to write the work up as a paper about six months after starting the project.
Learning dry methods makes it possible to examine a hypothesis before going to the bench, and to look at experimental data from another angle. Dry work does not only support wet research; it can produce results of its own, and the range of what a researcher can do grows.
I wanted to bring this experience to researchers who, like me, were unsure how to move their work forward. That wish became BaraPhaSilico.
We understand the reality of experimental research, and from there we support data analysis, hands-on in silico drug discovery, and the learning of new techniques. We walk alongside researchers as they combine their own expertise with new methods and take the work forward.
By bringing AI and IT together with a broad view of drug discovery, we hope to shorten the time it takes to find a medicine, and to help one more medicine reach the people who need it.
