We describe a deep learning-based system for predicting chemical reactions and identifying experimentally-observed masses.
Calculated methyl anion affinities are known to correlate with experimentally determined Mayr E parameters for individual organic functional group classes but not between neutral and cationic organic electrophiles. We demonstrate that methyl anion affinities calculated with a solvation model (MAA*) give a linear correlation with Mayr E parameters for a broad range of functional groups. Methyl anion affinities (MAA*), plotted on the log scale of Mayr E, provide insights into the full range of electrophilicity of organic functional groups. On the Mayr E scale, the electrophilicity toward the methyl anion spans 180 orders of magnitude. Article pubs.acs.org/joc
Methyl cation affinities are calculated for the canonical nucleophilic functional groups in organic chemistry. These methyl cation affinities, calculated with a solvation model (MCA*), give an emprical correlation with the Ns N term from the Mayr equation under aprotic conditions when they are scaled to the Mayr reference cation (4-MeOC 6 H 4 ) 2 CH + (Mayr E = 0). Highly reactive anionic nucleophiles were found to give a separate correlation, while some ylides and phosphorus compounds were determined to give a poor correlation. MCA*s are estimated for a broad range of simple molecules representing the canonical functional groups in organic chemistry. On the basis of a linear correlation, we estimate the range of nucleophilicities of organic functional groups, ranging from a C−C bond to a hypothetical tert-butyl carbanion, toward the reference electrophile to be about 50 orders of magnitude.
Four phthalazinones (CIDs 22334057, 22333974, 22334032, 22334012) and one isoquinolone (CID 5224943) were previously shown to be potent enhancers of antifungal activity of fluconazole against Candida albicans. Several even more potent analogues of these compounds were identified, some with EC 50 as low as 1 nM, against C. albicans. The compounds exhibited pharmacological synergy (FIC < 0.5) with fluconazole. The compounds were also shown to enhance the antifungal activity of isavuconazole, a recently FDA approved azole antifungal. Isoquinolone 15 and phthalazinone 24 were shown to be active against several resistant clinical isolates of C. albicans. KEYWORDS: Candida albicans, antifungal agents, fluconazole, synergy, phthalazinone, isoquinolone A zoles continue to be the drug of choice for many types of invasive fungal infections, acting on a key enzyme, sterol 14α-demethylase, in the ergosterol biosynthesis pathway. The azole family of antifungals has evolved continuously since the initial introduction of ketoconazole in the 1980s 1 with the goal of achieving high affinity toward the fungal P450 14α-demethylase, low affinity toward human CYP enzymes, 2,3 and, more recently, evasion of fungal resistance mechanisms. 4 Fluconazole, introduced in 1990, proved well-tolerated in patients and has been used ubiquitously to treat invasive candidiasis. 1 The emergence of fluconazole resistance has led to the increasing use of echinocandins and the development of third-generation azoles (voriconazole, posoconazole, isavuconazole) with higher affinity. 5 Of the third-generation drugs, posoconazole and voriconazole work against a broader range of fungal pathogens but are more expensive and have other disadvantages: posoconazole has a less flexible dosing and absorption profile than fluconazole; voriconazole may be ineffective against strains that have already developed resistance toward fluconazole. 6 Newer azole drugs like albaconazole and fosravuconazole are still in development.The development of improved azoles has been paralleled by the search for small molecules that enhance the antifungal effect of existing azoles, 7−10 but the efforts have been met with limited success. 11,12 A wide range of azole enhancers have been shown to exhibit antifungal synergy; 13 two approved drugs, flucytosine 14 and calcineurin inhibitors, 15−19 have been shown to synergize with fluconazole against at low concentrations (<10 μg/mL) against strains of C. albicans but have limitations for general use. Against other species, 20 flucytosine instead antagonizes the effect of fluconazole, so the benefits of flucytosine-azole combinations are not clear. 21 Calcineurin inhibitors such as sirolimus and tacrolimus depend on human CYP enzymes for metabolic clearance; azole drugs like fluconazole exert off-target effects on these human CYP enzymes. Buildup of these calcineurin inhibitors in plasma increases risk of nephrotoxicity and, as immunosuppressants, may increase rates of infection from other pathogenic fungal species. 22−24 Giv...
There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C−C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.