Forthcoming and Online First Articles

International Journal of Computational Biology and Drug Design

International Journal of Computational Biology and Drug Design (IJCBDD)

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International Journal of Computational Biology and Drug Design (8 papers in press)

Regular Issues

  • Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach   Order a copy of this article
    by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee 
    Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss.
    Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.

  • Molecular docking and pharmacophore modeling of phytoconstituents of Vaccinium secundiflorum for antidiabetic and antioxidant activity   Order a copy of this article
    by Jainey James, Afiya Abdul Aziz, Dhanya Krishnan, Pankaj Kumar, Abhishek Kumar 
    Abstract: Vaccinium secundiflorum of the Ericaceae family, a shrub in Madagascar, is used by the localities to treat diabetes mellitus. The study's main aim was to determine the binding interactions of the thirty phytoconstituents with the target proteins, 4GQQ and 1HD2, to assess their antidiabetic and antioxidant activities, respectively. In silico approaches by Schr
    Keywords: Phytoconstituents; Molecular docking; Pharmacophore modeling; ADMET; Antidiabetic; Vaccinium secundiflorum.

  • DSP Techniques for Protein Coding Region Identification based on Background Noise and Nonlinear Phase Delay Reduction from Period-3 Spectrum using Zero Phased Anti-Notch Filter and Savitzky-Golay (S-G) Filter   Order a copy of this article
    by Amit Kumar Singh, Vinay Kumar Srivastava 
    Abstract: Identifying protein-coding regions from a given DNA sequence has always been a challenging task in bioinformatics. Spectrum analysis techniques, such as short time discrete Fourier transform (STDFT) and anti-notch filters (ANF), have been successfully applied to solve this problem. The ANF techniques are comparatively faster than transform techniques. However, the filter based methods performance is still limited because of the excessive background noise and nonlinear phase delay in their outputs. The drawback with existing de-noising techniques is that it identically treats the coding regions spectra and noncoding regions spectra. Consequently, the discriminative spectral measure of protein coding regions is also diminished, which reduces the overall accuracy. A Savitzky-Golay (SG) acts as a weighted moving average filter that de-noise the signal without distortion. This paper investigated the de-noising performance of Savitzky-Golay (SG) filter with both the STDFT and ANF techniques. It is demonstrated that the ANF technique is more prone to background noise. To overcome the nonlinear phase delay in ANF spectrum, the zero phased anti-notch filter (ZANF) technique is used. The performance of the proposed method is compared with other de-noising techniques at the nucleotide level. The findings of this investigation encourage the combined use of ZANF followed and S-G filter as it provides the best prediction results than other methods.
    Keywords: Protein coding region prediction; 3-base periodicity; Short time discrete Fourier transform; Moving average filter; Savitzky-Golay(S-G) filter.

  • Telemetric Drug Injection System with Centralized Monitoring and Control of Multiple Injectors   Order a copy of this article
    by Maham Sarvat, Suhaib Masroor, Muhammad Muzammil Khan 
    Abstract: A Syringe pump is a device used for the administration of medicines, having a predetermined amount of doses, into the patients with prolonging injection time. The problem of continuous pump monitoring, drug status, injection time, and manual control are common drawbacks of all existing syringe pumps. In this study, all the aforesaid problems are addressed by the proposed syringe pump which offers wireless control of the pump from the nursing station via any android device having authorized access to the pump. Moreover, this study also proposes a centralized control of multiple syringe pumps, within 30-meter range, such that a single android device can access multiple syringe pumps installed in ICU/CCU, and perform all the drug injection control tasks. The proposed concept is executed by controlling two syringe pumps from a single android device. In this new design, a medical personal become able to control the entire multiple syringe pump system while staying at the nursing counter via an android device, instead of moving bed to bed to monitor and control the pumps. Thus, the proposed design also offer cost reduction in the term of wages paid to the medical personals required to control the pumps in the manual system
    Keywords: Telemedicine; Biomedical Instrumentation; Centralized Control; Syringe Pump.

  • Screening for novel lead compounds targeting benzodiazepine allosteric binding site of wild-type and H129Y variant of GABAA receptor: an in-silico molecular docking study   Order a copy of this article
    by Sai Manohar Thota, Naresh Krishna Narasimha 
    Abstract: Classical benzodiazepines (BZDs) bind at the high affinity BZD binding site of GABAA receptor and enhance the natural effect of GABA resulting in anxiolytic, anticonvulsant, muscle relaxation and sedative effects. However, BZDs have unwanted side effects due to drug interaction, abuse and dependence. In addition, GABAA receptor variants exhibit poor affinity for BZDs and could lead to pharmaco-resistance. Using virtual screening and in silico molecular docking, we have identified novel compounds with high affinity towards the BZD binding site of GABAA receptor. In addition, we have also identified compounds showing high affinity towards H129Y GABAA variant, which could act as novel personalized medicines. We have predicted physiochemical, pharmacokinetic and drug-likeness properties of the high affinity compounds. Further in vitro and in vivo validation could identify novel lead compounds against BZD allosteric binding site of wild-type and H129Y GABAA receptor variants.
    Keywords: GABAA receptors; Benzodiazepines; Allosteric binding site; Mutation; Free energy; Vibrational entropy; In silico docking; Flavonoids; Natural compounds; ADMET;.
    DOI: 10.1504/IJCBDD.2021.10042525
     
  • The Stock Assessment of Aristeus Antennatus Between a Protected Fishing Area and a Free Access Area   Order a copy of this article
    by Nossaiba Baba, Imane AGMOUR, Naceur ACHTAICH, Youssef EL FOUTAYENI 
    Abstract: This paper describes a model of the interaction between the AristeusrnAntennatus and Sardine marine species in two different areas: the first onernis a preserved area against fishing and the second one is a free accessrnfishing area. The Aristeus Antennatus in the preserved area grows accordingrnto the logistic model. If the Aristeus Antennatus population is in thernpreserved zone then it is protected against fishing but if not, i.e, if itrnis in the free acces fishing zone, it is captured. This paper has asrnobjective to study the existence and to prove the equilibrium pointsrnstability by using eigenvalues analysis. As results, we found that thernconditions that ensure the existence of the Aristeus Antennatus and Sardinernmarine populations are hold, and their coexistence is shown in the numericalrnsimulations results.
    Keywords: Sustainability of marine resources; Protected fishing area; Free access fishing zone; Aristeus antennatus population.

  • Identify Differentially Expressed Genes with Large Background Samples   Order a copy of this article
    by Jennifer Fowler, Jonathan Stubblefield, Jason Causey, Jake Qualls, Wei Dong, Hongmei Jiang, Karl Walker, Yuanfang Guan, Xiuzhen Huang 
    Abstract: To identify differentially expressed genes related to disease is important but challenging. The challenges include the inherent noisy nature of the collected data, as well as the imbalance between the very large number of genes and the relatively small number of collected study samples. To address some of these challenges, here we implemented the method of AUCg (Area Under the Curve gene ranking). The novelty of the implementation of AUCg is that it not only utilizes the study samples information but also makes good use of the large amount of publicly available gene expression samples as background. We applied AUCg to a private dataset of 217 multiple myeloma samples, compared to 36,754 publicly available gene expression samples. The analysis identified genes that could be potentially unique to multiple myeloma. The AUCg gene ranking method can be applied for studying many other cancers and human diseases, taking advantage of large publicly available data.
    Keywords: Genes; gene expression; samples; differentially expressed genes; multiple myeloma.

  • Detection of COVID-19 virus using Deep Learning   Order a copy of this article
    by Kewal Mehta, Hritik Patel, Vraj Patel, Ankit Sharma 
    Abstract: Corona Virus Disease of 2019 (COVID-19) is currently the most threatening and major medical challenge in the world. COVID-19 can be detected using X-ray and CT-scan images of the patients lungs. With the use of deep learning and neural networks, the process of classifying the patients CT-scan and X-ray images can be expedited. In this paper, we implemented Convolutional Neural Networks (CNN) for detection of COVID-19 in X-ray and CT-scan images of lungs. Several CNN architectures like VGG16, ResNet-50, Inception-v3, DenseNet 201, Xception, and InceptionResnet-v2 have been implemented and comparative analysis is presented. DenseNet 201 CNN architecture is found to be most accurate in detecting COVID-19 for both X-ray and CT-scan images. The quantitative results suggest promising results for the COVID-19 detection task.
    Keywords: COVID-19; X-ray; CT-scan; Deep Learning; Neural Networks; Convolutional Neural Network; Transfer Learning.