BMOS or Beyond Maximum Overlapping Set is our inventive new method for High Throughput Screening (HTS) analysis using graph-based small molecule chemical similarity clustering. BMOS is able to provide high quality results equivalent to fine-grained methods on very large datasets.
With BMOS you can identify important structural activity relationships, in seemingly dissimilar molecules, and gain important insights that positively impact your design of synthetic candidates, with reasonable job times and high levels of accuracy.
Fine grained clustering methods such as Maximum Overlaying Set (MOS) are very useful as they can identify important relationships in structural data at much lower similarity levels, but are computationally unattainable on large data sets, typical in today’s research projects. BMOS solves these computational roadblocks.
BMOS (Beyond Maximum Overlapping Set) combines a maximum common substructure (MCS) approach (coarse-grained) with a maximum overlapping set (MOS) approach (fine- grained), to create a totally new method.
BMOS can deal with large numbers of calculations on very large data sets, with reasonable job times and without compromising the quality of the results. BMOS is capable of MOS clustering for 20,000 structures with smaller compute resources and 80,000 structures with larger compute resources. This is a 10 fold increase on traditional MOS clustering numbers, with negligible loss of accuracy.
BMOS is robust, modular, and scalable and available now.