In this work, we carried out the design and synthesis of new chimeric compounds from the natural cytotoxic chalcone 2′,4′-dihydroxychalcone (2′,4′-DHC, A) in combination with cinnamic acids. For this purpose, a descriptive and predictive quantitative structure–activity relationship (QSAR) model was developed to study the chimeric compounds’ anti-cancer activities against human breast cancer MCF-7, relying on the presence or absence of structural motifs in the chalcone structure, like in a Free-Wilson approach. For this, we used 207 chalcone derivatives with a great variety of structural modifications over the α and β rings, such as halogens (F, Cl, and Br), heterocyclic rings (piperazine, piperidine, pyridine, etc.), and hydroxyl and methoxy groups. The multilinear equation was obtained by the genetic algorithm technique, using logIC₅₀ as a dependent variable and molecular descriptors (constitutional, topological, functional group count, atom-centered fragments, and molecular properties) as independent variables, with acceptable statistical parameter values ($R^2$ = 86.93, $Q^2$_LMO = 82.578, $Q^2$_BOOT = 80.436, and $Q^2$_EXT = 80.226), which supports the predictive ability of the model. Considering the aromatic and planar nature of the chalcone and cinnamic acid cores, a structural-specific QSAR model was developed by incorporating geometrical descriptors into the previous general QSAR model, again, with acceptable parameters ($R^2$ = 85.554, $Q^2$_LMO = 80.534, $Q^2$_BOOT = 78.186, and $Q^2$_EXT = 79.41). Employing this new QSAR model over the natural parent chalcone 2′,4′-DHC (A) and the chimeric compound 2′-hydroxy,4′-cinnamate chalcone (B), the predicted cytotoxic activity was achieved with values of 55.95 and 17.86 µM, respectively. Therefore, to corroborate the predicted cytotoxic activity compounds A and B were synthesized by two- and three-step reactions. The structures were confirmed by ¹H and ¹³C NMR and ESI+MS analysis and further evaluated in vitro against HepG2, Hep3B (liver), A-549 (lung), MCF-7 (breast), and CasKi (cervical) human cancer cell lines. The results showed IC₅₀ values of 11.89, 10.27, 56.75, 14.86, and 29.72 µM, respectively, for the chimeric cinnamate chalcone B. Finally, we employed B as a molecular scaffold for the generation of cinnamate candidates (C–K), which incorporated structural motifs that enhance the cytotoxic activity (pyridine ring, halogens, and methoxy groups) according to our QSAR model. ADME/tox in silico analysis showed that the synthesized compounds A and B, as well as the proposed chalcones C and G, are the best candidates with adequate drug-likeness properties. From all these results, we propose B (as a molecular scaffold) and our two QSAR models as reliable tools for the generation of anti-cancer compounds over the MCF-7 cell line.

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation ($R^2$ = 70.27, $R^N$= −0.008, $a\left(R^2\right)$= 0.014, and $\delta K$ = 0.021) and its prediction ability ($Q^2$_LMO= 67.89, $Q^2$_EXT = 67.75, $a\left(Q^2\right)$ = −0.068, $\delta Q$ = 0.0, $\overline{r_m{}^2}$ = 0.229, and $\Delta r_m{}^2$ = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules ($R^2$ = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

γ-amino butyric acid (GABA) is an inhibitory neurotransmitter whose deficiency has been associated with various neurological disorders. However, its low liposolubility limits its use as a supplement. Thus, multiple investigations have focused on searching for lipophilic GABA analogs that can modulate the activity of the GABA_B receptor, which could be associated with the etiology of some central nervous system disorders. The GABA analogs available on the market are Vigabatrin, Gabapentin as well as Pregabalin and Baclofen. In this work, we report on the synthesis of GABA analogs, taking the scaffold of GABA, Pregabalin, and Baclofen as a starting point. The analogs include structural features that could favor the affinity of the molecules for the GABA_B receptor, such as heterocyclic rings in the γ-position and alkyl or p-Cl-phenyl substituents (in analogy to Pregabalin and Baclofen, respectively). These analogs were synthesized by a sequence of reactions involving an N-alkylation, a 1,4-conjugated addition of dialkyl and diarylcuprates and a basic hydrolysis. Furthermore, a computational molecular docking over the GABA_B receptor was performed to evaluate the interaction of each compound in the Baclofen binding site. With this information, we evaluated our compounds as GABA_B agonists through a QSAR analysis. Finally, by means of molecular similarity analysis, and in silico ADME prediction, we support our three best compounds (8a–b, 8d) as potential GABA_B receptor agonists.

As the rate of discovery of new antibacterial compounds towards multidrug resistant bacteria is declining, there is an urge for the search of molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple mechanisms against antibiotics and is considered of critical priority. In this work we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = -0.008, aR2 = 0.014 and δK = 0.021) and its prediction ability (Q2LMO= 67.89, Q2EXT = 67.75, a(Q2)= -0.068, δQ = 0.0, rm2 = 0.229, and ∆rm2 = 0.522) were obtained with different statistical parameters; additional validation jobs were done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterial against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinical isolated strains of A. baumannii, as well as other multidrug resistant bacteria including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search of new antibacterial compounds against A. baumannii.