The EDEN platform, developed by Hans-Joachim Boehm, is a completely novel computational approach to drug discovery. Using either 2D structures of active compounds or the 3D protein structures of the target as input, EDEN generates a complex affinity fingerprint. This is then used to predict the activity of existing compounds and to design new compounds de novo with the desired biological activity.

For any given small molecule, EDEN can currently predict the binding to 3000 known drug targets and calculate the DMPK and  tox related properties. The use of many affinity fingerprints in parallel allows to not only search for active but also for selective molecules. The method can also be used to search for poly-functional compounds and it can be used for target identification.

We believe that EDEN delivers candidate compounds with very high accuracy and a low number of false positives because the computational model used to predict activity is more complete than other approaches. It uses both all “good bits” (required for binding) and all “bad bits” (detrimental to binding). This leads to very high enrichment factors.

The program is very fast and can presently handle roughly 500 million compounds in one day on a standard notebook.