Design, synthesis and evaluation of 3-arylidene azetidin-2-ones as potential antifungal agents against Alternaria solani Sorauer

Synthesis of Arylidene-β-lactams via exo-Selective Matsuda-Heck Arylation of Methylene-β-lactams

“…Compound 1e (see Figure and Introduction), obtained in 93% yield from the coupling of p -chlorophenyl-substituted β-lactam 5b and p -chlorobenzene diazonium salt 8o , has previously been synthesized in four steps via the Baylis-Hilman reaction and intramolecular nucleophilic substitution. In an antifungal activity assay, 1e was found to be the most active out of 28 compounds tested for their activity against the plant pathogen Alternaria solani Sorauer . All other coupling products 7 shown in Table have not been described in the literature so far.…”

“…Biological activities of arylidene-β-lactams are, however, not limited to β-lactamase inhibition: several compounds with this structural pattern (e.g., 1e ) are active against the fungal plant pathogen Alternaria solani Sorauer, the causal agent of early blight. This disease mainly affects potato and tomato plants and is responsible for severe economic losses . Compound 1d is an effective histamine-3-receptor-(H3R) agonist at nanomolar concentrations.…”

“…Previously reported syntheses of arylidene-β-lactams 1 , as for example the β-lactamase inhibitors SA-1-204 ( 1b ) or LN-1-255 ( 1c ), rely on a Wittig olefination of 6-oxo-penicillanic acid derivatives, which were in turn synthesized from 6-aminopenicillanic acid in a multistep synthesis . Alternative strategies (as for example used for the synthesis of 1e ) proceed via a late-stage β-lactamization of 2-aminomethyl cinnamates, which are accessible via a sequence of Baylis-Hillman reaction, dehydrative bromination, and nucleophilic substitution. ,, …”

Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

“…DM is included as a highly relevant descriptor in explaining the catalytic activity of enzymes in Quantitative Structure-Activity Relationships (QSAR) or Quantitative Structure-Property Relationships (QSPR) studies. For example: QSAR modeling of aromatase inhibition [26], antifungal activity [27], and HIV-1 protease/ cyclin-dependent kinases inhibition [28], QSAR modeling of aromatase inhibition [26], antifungal activity [27], and HIV-1 protease/ cyclin-dependent kinases inhibition [28], and in the estimation of micellar properties such as drug loading capacity (LC) [29] in QSPR model. Since DFT calculations are computationally too demanding for most large-scale virtual screening explorations, or for the incorporation in fast QSAR or QSPR models, so the relevant parameters are calculated by empirical or machine learning (ML) methods.…”

Machine learning for the prediction of molecular dipole moments obtained by density functional theory

“…A few recent examples are cited here [ 4 – 7 ]. In QSAR modeling of aromatase inhibition [ 4 ], antifungal activity [ 5 ], and HIV-1 protease/cyclin-dependent kinases inhibition [ 7 ], the molecular DM played a pivotal role as descriptor; it was calculated by molecular mechanics with the SYBYL program [ 4 ], or with DFT at the B3LYP/6-31G(d,p) [ 5 ] or B3LYP/6-31+G**(6d, 7f) [ 7 ] theory levels. An example of a QSPR model employing the DM descriptor is the estimation of micellar properties such as drug loading capacity (LC) [ 6 ], for which electronic structure factors and the DM were identified as the most important descriptors; in this case the DM was calculated with the DFT B3LYP functional and the 6-311G basis set.…”