Journal: Biochemistry

Article Title: The Rational Discovery of a Tau Aggregation Inhibitor

doi: 10.1021/acs.biochem.8b00581

Figure Lengend Snippet: Discovery of potentially active compounds through iterative machine learning. a) A pilot set of 1000 compounds randomly chosen from the CNS library was docked against the 10 most populated clusters from each of the 25 segments. The histogram of each compound’s most favored target illustrates that some segments of tau4RD are better able to accommodate small molecule binding. b) PLSR trained on 990 compounds from the pilot set was able to predict the docking scores of the remaining 10 compounds with reasonable accuracy. Pearson correlation coefficients are shown at the bottom of the panel. c) PLSR was used to select compounds from the diverse set of small molecules in the CNS library. Iteration 1 yielded 1000 compounds based on the results and fingerprints of the pilot set. Iteration 2, based on the docking scores and fingerprints of the 2000 compounds from the pilot set and Iteration 1, yielded primarily compounds that had already been examined and docked in Iteration 1. When these were excluded from the library, the next 1000 compounds (“Iter. 2 – new”) displayed worse docking scores on average. This indicated that the PLSR search had largely converged.

Article Snippet: Selected compounds were identified from the ChemBridge CNS small molecule collection, and dry samples were obtained through Hit2Lead (ChemBridge, San Diego, CA).

Techniques: Binding Assay