Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models

Sahu, Supriya; Ghosh, Surajit Kumar; Kalita, Jun Moni; Ginjupalli, Murali C; K, Kranthi Raj

doi:10.1016/j.ejps.2019.105208

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Therefore, all selected oxazole derivatives including N-substituted sulfonamide were found to be potent inhibitors against the cancer cell line via machine learning results. Recently, novel 1,3,5-triazine derivatives as antimalarial agents were designed by ML methods and synthesized [63]. It was performed supervised ML methods for 2D and 3D fingerprints of molecules which were reported on various articles, according to their biological activity to constitute 2D and 3D QSAR models, respectively.…”

Section: Supervised Learningmentioning

confidence: 99%

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Mermer

2021

Mol Divers

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Machine learning (ML) methods have attracted increasing interest in chemistry as in all fields of science in recent years. This method is of great importance for the design of targeted bioactive compounds, especially by avoiding loss of time, money, and chemicals. There are lots of online web-based platforms such as LibSVM and OCHEM for the application of ML methods. In this paper, it has been examined the literature data on the activity predictions of heterocyclic compounds, biological activity results such as antiurease, HIV-1 Integrase, E. Coli DNA Gyrase B, and antifungal, pharmacophore-based studies, synthesis, and finding possible inhibitors using different machine learning methods.

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Section: Supervised Learningmentioning

confidence: 99%

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Mermer

2021

Mol Divers

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“…Interestingly, the presence of 2° amino substituents and replacement of hydrophobic atoms with H‐bond donors such as piperazine led to an upsurge in the antimalarial activity. Similarly, the presence of “Cl” substituent at the phenyl ring anchored to thiazole moiety improved the bioactivity as compared to the unsubstituted hybrids (Sahu, Ghosh, Kalita, & Ginjupalli, 2020). The in silico investigations for appraising interactions between the test hybrids and active site of pf DHFR‐TS enzyme belonging to the chloroquine resistant Dd2 strain revealed that interactions with Asp54, Arg59, Arg122, Ile 164 residues improved the vulnerability of hybrids towards drug resistance, as these residues served as key points of mutations in the parasite (Sahu et al, 2019).…”

Section: Antiplasmodial Activity Of Hybrid Molecules Based On 135‐tmentioning

confidence: 99%

Hybrid molecules based on 1,3,5‐triazine as potential therapeutics: A focused review

Prasher

Sharma

Aljabali

et al. 2020

Drug Development Research

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Majority of the representative drugs customarily interact with multiple targets manifesting unintended side effects. In addition, drug resistance and over expression of the cellular efflux‐pumps render certain classes of drugs ineffective. With only a few innovative formulations in development, it is necessary to identify pharmacophores and novel strategies for creating new drugs. The conjugation of dissimilar pharmacophoric moieties to design hybrid molecules with an attractive therapeutic profile is an emerging paradigm in the contemporary drug development regime. The recent decade witnessed the remarkable biological potential of 1,3,5‐triazine framework in the development of various chemotherapeutics. The appending of the 1,3,5‐triazine nucleus to biologically relevant moieties has delivered exciting results. The present review focuses on 1,3,5‐triazine based hybrid molecules in the development of pharmaceuticals.

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“…Sahu et al used the KPLS ML method to model activities of compounds active against P. falciparum ( Sahu et al, 2020 ). They trained the model on 57 thiazolyl triazine derivatives and molecular fingerprint descriptors.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Winkler

2021

Front. Chem.

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Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

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Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models

Cited by 8 publications

References 10 publications

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Hybrid molecules based on 1,3,5‐triazine as potential therapeutics: A focused review

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

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