In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Jain, Nilanjana; Gupta, Swagata; Sapre, Neelima; Sapre, Nitin S.

doi:10.1039/c4ra15478a

Cited by 20 publications

(3 citation statements)

References 97 publications

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“…Further, for the nonlinear models, SVMs use nucleus mapping to transform into a high-dimension space for linear classification. At present, the SVM approach has been widely used in modeling at different scales for drug discovery [78].…”

Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…For the purpose of locating prospective novel drugs, AI-based QSAR approaches, such as decision trees, support vector machines, random forests, and linear discriminant analysis (LDA), have evolved from QSAR modelling tools [15,19,20].…”

Section: Ai In Drug Discoverymentioning

confidence: 99%

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

Ali,

Mohin,

Mondal

et al. 2024

Futur J Pharm Sci

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Background Artificial intelligence (AI) revolutionized the formulation and development of modern pharmaceuticals. With the help of AI, researchers can now optimize drug design, develop formulations, and streamline clinical trials in a much accurate and efficient way. Drug development might be greatly expedited and time-consuming procedure; however, with the help of AI this are significantly reduced. Main body of abstract The main advantages of AI in pharmaceutical formulation are its capacity to analyse vast amounts of data and spot patterns and connections that human researchers would miss. Various tools and technologies, such as ANN, fuzzy logic, neuro-fuzzy logic, and genetic algorithm are used for analysing the date, of which ANN is popular and mostly used. AI enables the discovery of novel pharmacological targets and the creation of more potent medications. AI may also be used to improve medication formulations by forecasting the solubility, stability, and bioavailability of drug candidates, increasing the likelihood that clinical trials will be successful. AI is also applied in designing clinical trials, reducing the time and cost of the process by identifying patient populations that are most likely to benefit from the treatment. Additionally, AI can monitor patients during clinical trials, detecting real-time adverse effects and adjusting dosages to improve patient outcomes. Conclusion AI is a potent pharmaceutical formulation and development tool, allowing researchers to analyse vast amounts of data, optimize drug formulations, and streamline clinical trials. As technology develops, experts anticipate that AI will increasingly show a crucial part in drug development, enabling faster, more efficient, and more effective treatments for various diseases.

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“…This article reports of studies of anthocyanin activity as an anti-SARS-Cov-2 used in silico approach. The study of antiviral activity using the in silico approach is one of the most effective and efficient alternatives in studying and predicting the interaction of drug candidates with protein targets [22][23][24][25][26]. In addition, the in silico approach is able to predict the physicochemical and biological activity of a drug candidate before a wet laboratory test is carried out [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

In silico approach: Anthocyanin derivatives as potential inhibitors of the COVID-19 main protease

Abdjan¹,

Rakhman²,

Handayani³

et al. 2020

Preprint

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In silico approach has been carried out for the determination of drug candidates from anthocyanin derivative as inhibitors of the COVID-19 main protease. Geometry optimization has performed using the DFT/B3LYP/6-31G(d,p) method as an initial step to prepare candidate ligand. The results of molecular docking showed that candidates C5 and C6 had promising results with a grid score smaller than the ligand reference (X77) with a flexible conformation type. Studies on absorption, distribution, metabolism, excretion, and toxicity of C5 and C6 candidates were conducted to study the physicochemical properties of drug candidates and to show good predictive results as drugs. Molecular dynamic simulation uses the ffSB14 force field for 200 ns to study the interaction between ligand and receptor, the system stability, solvent accessibility, energy interactions, and hydrogen bonds. The results show good interaction stability on the C5 complex compared to the reference ligand which is characterized by the binding free energy value of C5 was -42.77 ± 0.37 kcal/mol and X77 was -42.37 ± 0.41 kcal/mol.

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In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Cited by 20 publications

References 97 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Influence of artificial intelligence in modern pharmaceutical formulation and drug development

In silico approach: Anthocyanin derivatives as potential inhibitors of the COVID-19 main protease

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