Previously synthesized novel chalcone oxime ethers (COEs) were evaluated for inhibitory activities against monoamine oxidases (MAOs) and acetylcholinesterase (AChE). Twenty-two of the 24 COEs synthesized, except COE-17 and COE-24, had potent and/or significant selective inhibitory effects on MAO-B. COE-6 potently inhibited MAO-B with an IC₅₀ value of 0.018 µM, which was 105, 2.3, and 1.1 times more potent than clorgyline, lazabemide, and pargyline (reference drugs), respectively. COE-7, and COE-22 were also active against MAO-B, both had an IC₅₀ value of 0.028 µM, which was 67 and 1.5 times lower than those of clorgyline and lazabemide, respectively. Most of the COEs exhibited weak inhibitory effects on MAO-A and AChE. COE-13 most potently inhibited MAO-A (IC₅₀ = 0.88 µM) and also significantly inhibited MAO-B (IC₅₀ = 0.13 µM), and it could be considered as a potential nonselective MAO inhibitor. COE-19 and COE-22 inhibited AChE with IC₅₀ values of 5.35 and 4.39 µM, respectively. The selectivity index (SI) of COE-22 for MAO-B was higher than that of COE-6 (SI = 778.6 vs. 222.2), but the IC₅₀ value (0.028 µM) was slightly lower than that of COE-6 (0.018 µM). In reversibility experiments, inhibitions of MAO-B by COE-6 and COE-22 were recovered to the levels of reference reversible inhibitors and both competitively inhibited MAO-B, with K_i values of 0.0075 and 0.010 µM, respectively. Our results show that COE-6 and COE-22 are potent, selective MAO-B inhibitors, and COE-22 is a candidate of dual-targeting molecule for MAO-B and AChE.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.