Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors

Havránková, Eva; Peña‐Méndez, Eladia María; Csöllei, Jozef; Havel, Josef

doi:10.1016/j.bioorg.2020.104565

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…[14] Prediction of biological activity of compounds containing a 1, 3, 5-triazinyl sulfonamide. [15] Prediction of toxicity of compounds Prediction of toxicity of phenols.…”

Section: Prediction Of Different Types Of Activitymentioning

confidence: 99%

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Kaliuzhenko

Pushkarova²

2023

GoS

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Nowadays, modern analytical instruments allow producing great amounts of information for a large number of samples (objects) that can be analyzed in relatively short time. This leads to the availability of multivariate data matrices that require the use of mathematical and statistical procedures, in order to efficiently extract the maximum useful information from data. When processing the data obtained as a result of the chromatographic analysis and various spectroscopic methods, as well as sensory systems, such as the electronic nose and electronic tongue, one cannot avoid applying modern chemometric methods, e.g., pattern recognition and classification algorithms, discriminative analysis, and artificial neural networks.

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“…[14] Prediction of biological activity of compounds containing a 1, 3, 5-triazinyl sulfonamide. [15] Prediction of toxicity of compounds Prediction of toxicity of phenols.…”

Section: Prediction Of Different Types Of Activitymentioning

confidence: 99%

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Kaliuzhenko

Pushkarova²

2023

GoS

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“…Lolak et al prepared benzenesulfonamides incorporating 1,3,5-triazine substituted with anilines, or piperidine [ 21 ], which effectively inhibited hCA I and II with K i s of 51.67 ± 4.76 and 40.35 ± 5.74 nM, while ureidosulfonamides [ 22 , 23 ] inhibited hCA IX with K i s of 0.91–126.2 nM. Havránková et al [ 24 , 25 , 26 ] developed substituted s-triazines, containing sulfanilamide, homosulfanilamide, 4-aminoethylbenzenesulfonamide, piperazines as well as aminoalcohol moieties, inhibiting hCA IX with K i s in the range of 0.4–307.7 nM. Our working group, around Mikuš et al, has brought the first systematic study of 1,3,5-triazinyl-substituted benzenesulfonamides with amino acids as the substituents [ 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Synthesis and Inhibition Activity Study of Triazinyl-Substituted Amino(alkyl)-benzenesulfonamide Conjugates with Polar and Hydrophobic Amino Acids as Inhibitors of Human Carbonic Anhydrases I, II, IV, IX, and XII

Mikulová

Kružlicová

Pecher

et al. 2021

IJMS

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Primary sulfonamide derivatives with various heterocycles represent the most widespread group of potential human carbonic anhydrase (hCA) inhibitors with high affinity and selectivity towards specific isozymes from the hCA family. In this work, new 4-aminomethyl- and aminoethyl-benzenesulfonamide derivatives with 1,3,5-triazine disubstituted with a pair of identical amino acids, possessing a polar (Ser, Thr, Asn, Gln) and non-polar (Ala, Tyr, Trp) side chain, have been synthesized. The optimized synthetic, purification, and isolation procedures provided several pronounced benefits such as a short reaction time (in sodium bicarbonate aqueous medium), satisfactory yields for the majority of new products (20.6–91.8%, average 60.4%), an effective, well defined semi-preparative RP-C18 liquid chromatography (LC) isolation of desired products with a high purity (>97%), as well as preservation of green chemistry principles. These newly synthesized conjugates, plus their 4-aminobenzenesulfonamide analogues prepared previously, have been investigated in in vitro inhibition studies towards hCA I, II, IV and tumor-associated isozymes IX and XII. The experimental results revealed the strongest inhibition of hCA XII with low nanomolar inhibitory constants (Kis) for the derivatives with amino acids possessing non-polar side chains (7.5–9.6 nM). Various derivatives were also promising for some other isozymes.

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Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Strušnik

2021

Intl J of Energy Research

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In large thermal power facilities, thermal machines mainly fitted with steam turbines are used in thermodynamic process of converting thermal energy into mechanical work. In order to improve the efficiency of heat energy conversion into mechanical work, the increase of vacuum level in the condenser of a steam turbine was analyzed using the machine learning method. The vacuum in the steam turbine condenser increases after replacing the labyrinth gaskets with honeycomb ones, resealing the vacuum system and increasing the amount of cooling water through the condenser. The latter is controlled by algorithm, which is integrated into the steam turbine distribution control system. As a result of the increased vacuum in the steam turbine condenser, the enthalpy difference of the steam turbine low-pressure cylinder increases, and the steam turbine performs more mechanical work resulting in higher conversion efficiency. The drawback of the increased vacuum in the turbine condenser is that the flow of cooling water through the steam turbine condenser increases as well as heat dissipation from the condenser into the environment to be replaced by regenerative condensate heating. The results of the analysis show that the overall balance of the vacuum increase in the steam turbine condenser is positive, the steam turbine generates more power, and consequently the process efficiency increases by as much as 2.48 %.

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Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors

Cited by 5 publications

References 46 publications

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Synthesis and Inhibition Activity Study of Triazinyl-Substituted Amino(alkyl)-benzenesulfonamide Conjugates with Polar and Hydrophobic Amino Acids as Inhibitors of Human Carbonic Anhydrases I, II, IV, IX, and XII

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

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