Cresset’s innovative clustering method used to assess 3D similarity of 1.5 million BioBlocks structures

Cambridge, UK – 23rd February 2016 – Cresset Discovery Services, provider of contract research services for early phase discovery, has developed an innovative clustering method that made it possible to assess the 3D similarity across BioBlocks’ virtual database of over 1.5 million fragments. Cresset’s work enabled BioBlocks, a collaborative medicinal chemistry company, to design new fragment libraries with maximal coverage of 3D chemical space.

“Our goal was to help BioBlocks to build the maximum 3D diversity into small libraries of fragments from a starting pool of over a million compounds,” explains Dr Mark Mackey, CSO of Cresset. “Existing techniques would have required an unfeasible amount of computing power so we developed an entirely novel rapid clustering method. The solution was still extremely computationally challenging, but we were able to use our expertise in distributing calculations to the cloud to deliver the results that BioBlocks needed on time and within budget.”

“Working with Cresset has been a positive experience from start to finish,” said Warren Wade, VP of Chemistry at BioBlocks. “Because our fragments are designed to be new chemical matter, they challenged the limits of existing structural descriptions. Cresset worked closely with us to overcome these limits and produce a high value compound set.”

The final result is a 3D fragment library that contains a significant number of compounds with novel core structures that are now viable targets for fragment generation. BioBlocks plans to release the first set of plates for fragment screening over the course of 2016.

BioBlocks envisions this comprehensive fragment library to be a drug discovery tool available only to collaborators who will be able to leverage this new chemical space for their lead discovery programs and utilize BioBlocks’ collaborative medicinal chemistry processes that were developed to increase the probability of generating commercially viable leads.

3D similarity-based clustering workflow