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 (4 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.

  • Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics   Order a copy of this article
    by Clayton Fernando Rencilin, Joseph Christina Rosy, Krishnan Sundar 
    Abstract: Leishmaniasis, a life-threatening tropical disease, is caused by 20 different species of Leishmania parasites. The disease, that is endemic in nearly 100 countries, contributes to millions of deaths each year. However, very few anti-leishmanial compounds are available in the market and that too possess many drawbacks. Hence, the therapeutic arsenal requires potential and novel anti-leishmanial compounds to treat Leishmaniasis. In the present study, Quantitative Structure Activity Relationship (QSAR) model and Pharmacophore model were developed with a set of anti-leishmanial compounds collected from literature and commercial anti-leishmanial drugs. Novel compounds matching the Pharmacophore was screened which can be potentially be used to treat Leishmaniasis. The compounds were segregated into training and test sets and, QSAR models were developed by Multiple Linear Regression (MLR) method using EasyQSAR. Further, pharmacophore model was derived by physio-chemical features of the selected compounds using PharmaGist. Using MLR, different QSAR models were developed using various molecular descriptors. Further, the percentage contribution of descriptors on each model was studied. The models were validated using the test sets with statistical measures. A ligand-based pharmacophore model was developed using active compound as template. The pharmacophore model was used for searching the purchasable compound dataset of ZINC database for matching compounds. The theoretical activities, ADME and drug likeness properties of top compounds were analyzed. Thirteen novel, readily purchasable compounds were obtained from this approach, which shows good predicted activity, ADME and druglikeness. These compounds can be regarded as potential candidates to be developed as novel anti-leishmanial drugs with improved activity and reduced side effects.
    Keywords: Antileishmanial compounds; descriptor; pharmacophore; ZINCPharmar; Pharmacophore search and QSAR.

  • Random Forest with SMOTE and Ensemble Feature Selection for Cervical Cancer Diagnosis   Order a copy of this article
    by Anjali Kuruvilla, B. Jayanthi 
    Abstract: Cervical tumours are a leading cause of death worldwide, although they can be prevented by removing afflicted tissues early on. Recognizing population weaknesses is necessary for inclusive cervical screening programmes. STDs and smoking cause cervical cancer. Creating a cancer classifier requires complex learning. FS decreases a prediction system's inputs. Reducing model parameters and time improves performance. The effort aims to design a new Ensemble Feature Selection (EFS) and classifier for cervical cancer diagnosis. EFS, several FSs have been used. EFS mixes the results of single FS approaches, including EEHO, EBFO, and RFE, to improve results. The results of EFS are combined via the Bootstrap aggregation function. Random Forest (RF) with SMOT is the classifier technique (SMOTE). UCI's cervical cancer database comprises 32 features and 4 classes. Using a confusion matrix and evaluation criteria including precision, recall, f-measure, and accuracy, classification performance is calculated. The classification methods have been implemented via the MATrix LABoratory (MATLAB) simulator. The proposed algorithm gives an enhanced accuracy value of 94.7552%, 94.5221%, 94.8718%, and 94.2890 % for the Hinselmann, Schiller, Citology, and Biopsy tests, respectively.
    Keywords: Cervical cancer; Ensemble Feature Selection (EFS); Entropy Elephant Herding Optimization (EEHO); Entropy Butterfly Optimization Algorithm (EBFO); Recursive Feature Elimination (RFE) Random Forest (RF).
    DOI: 10.1504/IJCBDD.2023.10053107
     
  • Virtual screening of plant phytochemicals to discover potent Janus Kinase-1 inhibitors against severe COVID-19 and sepsis.   Order a copy of this article
    by Shradheya R. R. Gupta, Kavita Joshi, Subham Verma, Rakesh Sharma, Sameer Qureshi, Mansoor Ali Syed, Vandana Nunia 
    Abstract: Janus kinases (JAK) are intracellular tyrosine kinases that transduce cytokine-mediated signals. They play a major role in sepsis and SARS-COVID-19 virus-induced MODS (Multiple Organ Dysfunction Syndrome) progression. Therefore, inhibition of these kinases might be an efficient option for the treatment of sepsis and MODS (like acute respiratory distress syndrome, acute liver injury, etc.). In the absence of notable success for the treatment of these diseases, the current study was focused on finding the potential phytochemicals to inhibit JAK1. We prepared and screened a library of 5229 diverse phytochemicals. On the basis of drug likeness properties (Rule of 5) and ADMET, 2081 phytochemicals were filtered out. These compounds were docked with the JAK1 kinase domain and arranged in their descending binding energy. Upadacitinib, a FDA approved JAK-1 inhibitor was set as a reference in the current study. To further shortlist the compounds from the list, the energy cut-off was set to -11 Kcal/mol, which was higher than Upadacitinib. The top four compounds Kudzuisoflavone B, Taiwaniaflavone 7-O-methyl ether, Formosanatin D, and Withaphysalin A, showed binding energy -12 Kcal/mol, higher than cut-off value were further piped for dynamic simulation. From these four compounds, Kudzuisoflavone B was selected based on the RMSD, RMSF, number of H-bond, hydrophobic interactions, MMPBSA and GROMACS total energy.
    Keywords: Janus kinase; SARS-CoV-2 virus; Sepsis; Virtual drug screening; Phytochemical inhibitors.