Improving the analysis of biological ensembles through extended similarity measures

Abstract

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically...

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets

“…The twenty more representatives (medoids) per library (including the three in Figure 8) are listed in Table S2 in the Supporting Information. The medoids are calculated using the algorithm described recently [10] . In short, we calculate the complementary similarity of every molecule in a library, that is, the similarity of all but the selected molecule (which we can do in O(N) for the whole set).…”

“…Extended similarity has been successfully used in numerous applications, including compound diversity analysis, [21] comparison of nucleotide and protein sequences, [22] and, more recently, analysis of molecular dynamics simulations. [10] Herein, we used this approach as a novel and efficient manner to quantify and compare the fingerprint-based diversity of the 11 compound libraries. The extended similarity indices were computed with a fractional weight function, with various coincidence thresholds.…”

“…The medoids are calculated using the algorithm described recently. [10] In short, we calculate the complementary similarity of every molecule in a library, that is, the similarity of all but the selected molecule (which we can do in O(N) for the whole set). Then, we can rank all the molecules from more (e. g., medoid-like) to less (e. g., outlier-like) representative by simply ordering them according to the increasing value of their complementary similarity.…”

“…Second, the extended indices are dramatically more efficient, requiring only O(N) operations to calculate the similarity of N molecules (opposed to the quadratic scaling of the standard binary similarity indices). Extended similarity has been successfully used in numerous applications, including compound diversity analysis, [21] comparison of nucleotide and protein sequences, [22] and, more recently, analysis of molecular dynamics simulations [10] . Herein, we used this approach as a novel and efficient manner to quantify and compare the fingerprint‐based diversity of the 11 compound libraries.…”

“…For each epigenetic‐focused data set, we identified the most frequent core structures. We also generate novel representations of the chemical space using the concept of Chemical Library Networks (CLNs) [9] and rapidly identify the most representative individual molecules (i. e., medoids) in a large data set: these chemical structures can be used as a “chemical structural marker” or “chemical diagnostic molecules.” [10] Herein, we discuss the most representative chemical structures of the epigenetic libraries that can be used as a criterion for comparing the chemical libraries and prioritization for follow‐up studies, including computational and experimental screening.…”