Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties

Chandrasekaran, Balakumar; Abed, Sara Nidal; Al-Attraqchi, Omar; Kuche, Kaushik; Tekade, Rakesh K.

doi:10.1016/b978-0-12-814421-3.00021-x

Cited by 122 publications

(89 citation statements)

References 72 publications

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“…In silico ADMET prediction studies are a central component of pharmaceutical research and drug design, as it provides helpful guidance in the early evaluation of the in vivo efficiency and safety of a drug. The magnitude of the biological activity of an active molecule, in terms of specific interactions with the molecular target, along with the drug-related side effects, are strongly influenced by its pharmacokinetic properties [21]. Integrated ADMET prediction platforms allow an early research stage determination of the molecular descriptors, which predicts the suitability of a small molecule for oral administration, thus reducing the number of synthesis–evaluation cycles and the more expensive late-stage failures [22].…”

Section: Resultsmentioning

confidence: 99%

Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin

et al. 2019

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In the context of there being a limited number of clinically approved drugs for the treatment of Candida sp.-based infections, along with the rapid development of resistance to the existing antifungals, two novel series of 4-phenyl-1,3-thiazole and 2-hydrazinyl-4-phenyl-1,3-thiazole derivatives were synthesized and tested in vitro for their anti-Candida potential. Two compounds (7a and 7e) showed promising inhibitory activity against the pathogenic C. albicans strain, exhibiting substantially lower MIC values (7.81 μg/mL and 3.9 μg/mL, respectively) as compared with the reference drug fluconazole (15.62 μg/mL). Their anti-Candida activity is also supported by molecular docking studies, using the fungal lanosterol C14α-demethylase as the target enzyme. The interaction of the most biologically active synthesized compound 7e with bovine serum albumin was investigated through fluorescence spectroscopy, and the obtained data suggested that this molecule might efficiently bind carrier proteins in vivo in order to reach the target site.

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Section: Resultsmentioning

confidence: 99%

Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin

et al. 2019

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“… 13 These features are numerical in nature and describe the physical and chemical properties of molecules. 17 2D descriptors represent information related to size, shape, distribution of electrons, octanol–water distribution coefficients (log P ) measuring lipophilicity, nAromAtom denoting the number of aromatic atoms, nHeavyAtom denoting the number of non-hydrogen atoms, and nBondsT denoting the number of triple bonds. 3D descriptors relate to the 3D conformation of the molecules and include the moment of inertia along the Y axis (MOMIY).…”

Section: Methodsmentioning

confidence: 99%

Quantitative Toxicity Prediction via Meta Ensembling of Multitask Deep Learning Models

et al. 2021

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Toxicity prediction using quantitative structure–activity relationship has achieved significant progress in recent years. However, most existing machine learning methods in toxicity prediction utilize only one type of feature representation and one type of neural network, which essentially restricts their performance. Moreover, methods that use more than one type of feature representation struggle with the aggregation of information captured within the features since they use predetermined aggregation formulas. In this paper, we propose a deep learning framework for quantitative toxicity prediction using five individual base deep learning models and their own base feature representations. We then propose to adopt a meta ensemble approach using another separate deep learning model to perform aggregation of the outputs of the individual base deep learning models. We train our deep learning models in a weighted multitask fashion combining four quantitative toxicity data sets of LD 50 , IGC 50 , LC 50 , and LC 50 -DM and minimizing the root-mean-square errors. Compared to the current state-of-the-art toxicity prediction method TopTox on LD 50 , IGC 50 , and LC 50 -DM, that is, three out of four data sets, our method, respectively, obtains 5.46, 16.67, and 6.34% better root-mean-square errors, 6.41, 11.80, and 12.16% better mean absolute errors, and 5.21, 7.36, and 2.54% better coefficients of determination. We named our method QuantitativeTox, and our implementation is available from the GitHub repository .

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“…As a reference, properties of broflanilide were also measured. For lipophilicity, most commonly referred to as LogP [ 29 ], replacing the heptafluoroisopropyl group with the SF 5 moiety resulted in similar LogP values in broflanilide and the meta-diamide 4d (entry 1, Table 3 ). In addition, both the molecules meta-diamide 4d and broflanilide showed high levels of kinetic solubility [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

Synthesis and Properties of Pentafluorosulfanyl Group (SF5)-Containing Meta-Diamide Insecticides

et al. 2020

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Herein, we describe novel pentafluorosulfanyl (SF5) group-containing meta-diamide insecticides. For the facile preparation of the SF5-based compounds 4a–d, practical synthetic methods were applied. Among newly synthesized compounds, 3-benzamido-N-(2,6-dimethyl-4-(pentafluoro-λ6-sulfanyl)phenyl)-2-fluorobenzamide 4d showed (i) a high insecticidal activity, (ii) an excellent selectivity to insects, and (iii) good levels of water solubility and log P values. In this study, we demonstrated that the pentafluorosulfanyl moiety could serve as an attractive functionality for the discovery of a new scope of crop-protecting agents.

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Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties

Cited by 122 publications

References 72 publications

Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin

Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin

Quantitative Toxicity Prediction via Meta Ensembling of Multitask Deep Learning Models

Synthesis and Properties of Pentafluorosulfanyl Group (SF5)-Containing Meta-Diamide Insecticides

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