Automation and Scaling of Analytical Processes

How one client was able to reduce 3-4 weeks’ work to real-time



When a client came to Aigenpulse for help with in-silico target validation, their established analytics routine took 3-4 weeks per target. Their data exploration was hampered by external data and analytics tools, which couldn’t be combined efficiently with in-house data assets.

Without the ability to rapidly analyse large data assets, the client could not drive important development decisions. Ineffective in-silico target validation kept slowing them down, and every additional data parameter slowed them down even more.

We offered them a solution, and this is how we did it.


Since our client already had a lot of in-house data and relied on external datasets, we imported these assets into the Aigenpulse Platform. To maximise data handling efficiency, analytics were also integrated into the client’s package.

Once everything was accessible via the unified interface and API, we started to optimise the analytics workflow. The performance tuning data structures and machine learning improved efficiency and scalability of reliable complex analytics procedures.

The fully automated analytics made in-silico target validation as rapid as possible. Now, the Aigenpulse Platform offers real time data analysis and generates reports upon every data import.


The Aigenpulse Platform integration cut down the data analysis timeframe from 3-4 weeks to real time. The fully automated in-silico validation routine increased data processing efficiency, and it keeps saving our client’s time.

In this case, our client needed the Aigenpulse Platform to identify targets for biologics development. Now they have a tool to efficiently choose and progress validated leads, without wasting time on lengthy analytics or loosing promising signals across large datasets.