Compound Acquisition and Prioritization Algorithm for Constructing Structurally Diverse Compound Libraries

In the present study, we report a compound acquisition and prioritization algorithm established for rational chemical library purchasing or compound synthesis to increase the diversity of an existing compound collection. This method was established based on a chemistry-space calculation using BCUT (Burden CAS University of Texas) descriptors. To identify the acquisition of compounds from candidate collections into the existing collection, a derived distance-based selection rule was applied, and the results were well supported by pairwise similarity calculations and cell-partition statistics in chemistry space. The correlation between chemistry-space distance and Tanimoto similarity index was also studied to justify the compound acquisition strategy through weighted linear regression. As a rational approach for library design, the distance-based selection rule exhibits certain advantages in prioritizing compound selection to enhance the overall structural diversity of an existing in-house compound collection or virtual combinatorial library for in silico screening, diversity oriented synthesis, and high-throughput screening.