Research ArticleOpen accessIn the abstractUnbalance Quantitative Structure Activity Relationship Problem Reduction in Drug DesignD. Pugazhenthi, S. P. RajagopalanProblem statement: Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcome the disadvantage of high cost and long cycle by employing traditional experimental methods. With the fact that number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict molecular activities considering such an unbalanced … …Journal of Computer Science, Vol. 5, Issue 10 · 31 Oct 2009 · 6,704 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Feasibility Study of Challenges and Opportunities in Computational Biology: A Malaysian PerspectiveR. J.A. Richard, N. SriraamThe term computational biology refers to the knowledge derived from a computer analysis of biological data that includes identification of genes in DNA sequence of different organisms, prediction of structural and functional mechanism of proteins, feature extraction and classification of genomics and proteomics. Computational biology is a rapidly developing branch of science and is highly interdisciplinary, using techniques and Concepts … …American Journal of Applied Sciences, Vol. 2, Issue 9 · 30 Sep 2005 · 6,924 accessesView PDFCite
Research ArticleOpen accessIn the abstractDensity Functional Theory Based Quantitative Structure Activity Relationship Study of 2,5-Bis(1-Aziridinyl)-p-Benzoquinones with Lymphoid LeukemiaBahjat A. Saeed, Rita S. EliasProblem statement: QSAR techniques increase the probability of success and reduce time and coast in drug discovery process. The study presented QSAR investigation on 32 bioactive aziridinylbenzoquinones that have activity against lymphoid leukemia. Approach: Molecular descriptors, molecular weight, total energy, hardness, chemical potential, electrophilicity index, HOMO and LUMO energies were calculated. Initial geometry optimizations were carried out with the AM1 … …American Journal of Applied Sciences, Vol. 7, Issue 7 · 31 Jul 2010 · 7,211 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordAnalyzing Temperature-Dependent Thermal Properties of Titanium Aluminide Using ANN Predictive ModelingArmaghan Shalbaftabar, Kristen Rhinehardt, Dhananjay KumarThis study presents a comprehensive analysis of the thermal behavior of Titanium Aluminide (TiAl) across a range of temperatures using an Artificial Neural Network (ANN) predictive model. The study investigates various material properties of TiAl, including Band Gap, Young Module, Density, Energy Absorption, Thermal Conductivity, and Specific Heat, at different temperature points. The ANN model accurately captures the temperature-dependent trends … …American Journal of Engineering and Applied Sciences, Vol. 17, Issue 4 · 20 Nov 2024 · 4,308 accessesView PDFCite