The compounds are uniformly distributed round the regression collection (Figure 1), which suggests the obtained magic size has satisfactory predictive ability. Open in a separate window Figure 1 Quantitative structure-activity relationship (QSAR) magic size obtained with the heuristic method for 15 molecules with the CODESSA software (R2 = 0.7945, F = 14.18, S2 = 0.0243). glycosylated chalcone 7b was the most potent compound against this species. Probably the most encouraging compounds were not harmful to of previously explained chalcones, it PK 44 phosphate seemed that the presence of a polymethoxylated B-ring could be important for this activity . Moreover, the intro of a triazole moiety is definitely associated with an increase in AF activity . In fact, over the last decade, there has been a great desire for the synthesis of 1,2,3-triazoles due to the fact of these moieties behaved as more than passive linkers. They carried beneficial physicochemical properties, showing importance to biological activity [11,12]. This approach has been used to generate a vast array of compounds with biological potential [13,14,15,16], namely with AF activity [10,17,18]. Moreover, some antimicrobial providers are based on nitrogen heterocycles, including the triazole-based biocides fluconazole and itraconazole, which suggest their potential to act as AF providers . Based on this, the present work seeks to synthesize fresh potential AF polymethoxylated chalcone and flavone derivatives with glycosyl organizations incorporating a 1,2,3-triazole moiety using a click chemistry approach. The potential of synthesized compounds as benign AF providers was assessed against the adhesive larvae of the macrofouling mussel and the biofilm-forming marine bacteria and The most promising compounds were submitted to complementary assays to evaluate their viability as AF providers, including the evaluation of possible mechanisms of action related with adhesion and neurotransmission pathways. These compounds were also tested for anti-microalgal activity towards sp. and general ecotoxicity using nauplii of the marine shrimp larvae at 50 M was assessed. In this testing bioassay, in addition to glycosylated flavones 3a, 3b, 4a and 4b and chalcones 7a, 7b, 8a and 8b, non-glycosylated flavones 1aCb and 2aCb and chalcones 6aCb, 10aCb and 11aCb were tested in order to perform SAR studies. Results showed that among 18 tested flavonoids (10 chalcones and 8 flavones), seven chalcones (6a, 6b, 7b, 8a, 8b, 11a and 11b) and only three flavones (1b, 4a and 4b) offered a percentage of arrangement 40%, suggesting that chalcone scaffold seems to be more encouraging for anti-settlement activity. These 10 compounds were further selected for doseCresponse studies in order to determine LC50/EC50 ideals. Among these, three chalcones (7b, 11a, 11b) and one flavone (1b) exposed effective anti-settlement activity (EC50 25 g/mL), with triazolyl glycosylated chalcone 7b EXT1 becoming the most potent (EC50 = 3.28 M; 2.43 gmL?1), showing the highest therapeutic percentage ( 60.98) (Table 1). Table 1 Antifouling (AF) performance and toxicity guidelines of flavones 1b, 4a and 4b and chalcones 6a, 6b, PK 44 phosphate 7b, 8a, 8b, 11a and 11b towards mussel PK 44 phosphate plantigrade larvae. plantigrades of the tested flavonoids. In this work, a 2D-QSAR model was elaborated using the Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA 2.7.2) software package, which calculates approximately 500 descriptors. The heuristic method performs a pre-selection of descriptors by eliminating descriptors that are not available for each structure, that have a small variance in magnitude, that are correlated pairwise, and that have no statistical significance. The heuristic method is a very useful method for searching the best set of descriptors, without restrictions on the data arranged size . The correlation coefficient (R2), squared standard error (S2), and Fishers value (F) were used to evaluate the validity of regression equation . As the rules of QSAR set up that there should be one descriptor for each five molecules used to build the model , three descriptors were used to build the QSAR equation. The multilinear regression analysis using Heuristic method for 15 compounds in the three-descriptor model is definitely shown in Number 1. The compounds are uniformly distributed round the regression collection (Number 1), which suggests that the acquired model has adequate predictive ability. Open in a separate window Number 1 Quantitative structure-activity relationship (QSAR) model acquired with the heuristic method for 15 molecules with the CODESSA software (R2 = 0.7945, F = 14.18, S2 = 0.0243). X, X and stands for solvent-accessible surface area of H-bonding acceptor atoms, selected by threshold charge. This descriptor shows the importance of the hydrogen bonding acceptor properties for the activity of the test compounds . The topological descriptor average complementary information content of order 2 (CIC2).