Double Variational Binding—(SMILES) Conformational Analysis by Docking Mechanisms for Anti-HIV  Pyrimidine Ligands

Putz, Mihai V.; Dudaş, Nicoleta A.; Isvoran, Adriana

doi:10.3390/ijms160819553

Cited by 7 publications

(5 citation statements)

References 121 publications

(478 reference statements)

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“…18 Uracil pharmacophore is well known in anti-cancer (5-fluorouracil, tegafur), 19 anti-herpes virus (trifluridine), 20 and antidiabetic (linagliptin and alogliptin) drugs. 21 The importance of the uracil scaffold in anti-HIV drugs is widely recognized with reports of uracil hybrid molecules as NNRTIs; 22–25 however, there is no commercial uracil-based CA inhibitor.…”

Section: Introductionmentioning

confidence: 99%

Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies

Ramesh

Mohanty

et al. 2022

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies

Ramesh

Mohanty

et al. 2022

RSC Adv.

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“…As such an attractive and important target, HIV-1 integrase (IN) is an essential enzyme in the HIV-1 lifecycle responsible for inserting the reverse-transcribed viral genome into the host DNA through 3′ processing (3′-P) and strand transfer (ST) reaction [ 15 , 16 ]. Unlike PR and RT, there is neither known functional analog of IN in human cells nor apparent cellular toxicity for IN inhibitors [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

Zhou

Hao

Peng

et al. 2021

Computational and Mathematical Methods in Medicine

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A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors.

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“…The preliminary ‘interaction generation’ analysis of the InhA active site with no ligand bound (PDB: 4DRE, Figure 1C) revealed at least four HYD features, two of them located in the LHP. Structure–activity relationships involving interactions of 3D pharmacophore have been previously reported for HIV-1 inhibition, genetic disorders treatment, or proton pump inhibition [19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Design and in Silico Screening of Virtual Combinatorial Library of Benzamides Inhibiting 2-trans Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Predicted Pharmacokinetic Profiles

Kouman

Keita

N'Guessan

et al. 2019

IJMS

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Background: During the previous decade a new class of benzamide-based inhibitors of 2-trans enoyl-acyl carrier protein reductase (InhA) of Mycobacterium tuberculosis (Mt) with unusual binding mode have emerged. Here we report in silico design and evaluation of novel benzamide InhA-Mt inhibitors with favorable predicted pharmacokinetic profiles. Methods: By using in situ modifications of the crystal structure of N-benzyl-4-((heteroaryl)methyl) benzamide (BHMB)-InhA complex (PDB entry 4QXM), 3D models of InhA-BHMBx complexes were prepared for a training set of 19 BHMBs with experimentally determined inhibitory potencies (half-maximal inhibitory concentrations IC50exp). In the search for active conformation of the BHMB1-19, linear QSAR model was prepared, which correlated computed gas phase enthalpies of formation (∆∆HMM) of InhA-BHMBx complexes with the IC50exp. Further, taking into account the solvent effect and entropy changes upon ligand, binding resulted in a superior QSAR model correlating computed complexation Gibbs free energies (∆∆Gcom). The successive pharmacophore model (PH4) generated from the active conformations of BHMBs served as a virtual screening tool of novel analogs included in a virtual combinatorial library (VCL) of compounds containing benzamide scaffolds. The VCL filtered by Lipinski’s rule-of-five was screened by the PH4 model to identify new BHMB analogs. Results: Gas phase QSAR model: −log10(IC50exp) = pIC50exp = −0.2465 × ∆∆HMM + 7.95503, R2 = 0.94; superior aqueous phase QSAR model: pIC50exp = −0.2370 × ∆∆Gcom + 7.8783, R2 = 0.97 and PH4 pharmacophore model: pIC50exp = 1.0013 × pIC50exp − 0.0085, R2 = 0.95. The VCL of more than 114 thousand BHMBs was filtered down to 73,565 analogs Lipinski’s rule. The five-point PH4 screening retained 90 new and potent BHMBs with predicted inhibitory potencies IC50pre up to 65 times lower than that of BHMB1 (IC50exp = 20 nM). Predicted pharmacokinetic profile of the new analogs showed enhanced cell membrane permeability and high human oral absorption compared to current anti-tuberculotics. Conclusions: Combined use of QSAR models that considered binding of the BHMBs to InhA, pharmacophore model, and ADME properties helped to recognize bound active conformation of the benzamide inhibitors, permitted in silico screening of VCL of compounds sharing benzamide scaffold and identification of new analogs with predicted high inhibitory potencies and favorable pharmacokinetic profiles.

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Double Variational Binding—(SMILES) Conformational Analysis by Docking Mechanisms for Anti-HIV Pyrimidine Ligands

Cited by 7 publications

References 121 publications

Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies

Uracil derivatives as HIV-1 capsid protein inhibitors: design, in silico, in vitro and cytotoxicity studies

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

Structure-Based Design and in Silico Screening of Virtual Combinatorial Library of Benzamides Inhibiting 2-trans Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Predicted Pharmacokinetic Profiles

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