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2-METHYLIMIDAZOLIUM PYRIDINE-2,5-DICARBOXYLATO ZINC(II) DIHYDRATE: SYNTHESIS, CHARACTERIZATION, DNA INTERACTION, ANTI-MICROBIAL, ANTI-OXIDANT AND ANTI-BREAST CANCER STUDIES
(Taylor & Francis Online, 2021-09-25) Dhakshinamoorthy, Sudha; Sundararajan, Vairam; Subbarayan, Sarathbabu; Nachimuthu, Senthil Kumar; Ramasamy, Sivasamy; Suyambulingam, Jone Kirubavathy
2-Methylimidazolium pyridine-2,5-dicarboxylato zinc(II) dihydrate crystal (1) is synthesized and characterized by Fourier transform infrared spectroscopy (FTIR), single crystal X-ray diffraction analysis (SCXRD), thermogravimetric-differential thermal analysis (TG-DTA), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDAX), powder X-ray diffraction analysis (PXRD), proton nuclear magnetic resonance (1H NMR) studies, electronic absorption studies (UV-VIS) and DNA interaction studies. 1 was then explored for anti-microbial, anti-oxidant and anti-cancer activity. The SCXRD studies show that the compound crystallizes in the triclinic system and exhibits a distorted octahedral geometry with methyl imidazole ion as the cation. An exothermic decomposition at 400 °C implies high temperature stability in TG-DTA. PXRD confirms the phase purity of the sample. 1H NMR and UV-VIS results show that the solution structure of 1 is in agreement with SCXRD data. DNA interactions evaluated by agarose gel electrophoresis method substantiate the intercalative mode of binding. Anti-oxidant analysis shows that it exhibits good scavenging ability against DPPH and NO radicals. Anti-microbial activity suggests that 1 has better activity against Escherichia coli than Staphylococcus aureus. Further, the potential anti-cancer activities of complex indicate that the compound has good activity with a half-maximal inhibition concentration (IC50) value of 21.3 against MCF-7 human breast cancer cell line, suggesting that it may act as an anti-breast cancer drug.
3D NANOMANIPULATION: DESIGN AND APPLICATIONS OF FUNCTIONAL NANOSTRUCTURED BIO-MATERIALS
(IOP Publishing Ltd, 2020) Lega, P V; Orlov, A P; Frolov, A V; Subramani, R; Irzhak, A V; Koledov, V V; Smolovich, A M; Shelyakov, A V
Recent progress in the development of the new functional materials opens up exciting possibilities for designing reconfigurable micro- and nano-structures and for operating mechanical nanotools which are controlled by external fields or heat. The nanotools such as nanotweezers with an active layer thickness of about several tenths of nm, and whose overall size is of the order of 1 μm can be applied to different micro- and nanoobjects. The present report gives an overview of the application of mechanical nanotools in 3D nanomanipulation of bio-nano objects such as micro biofibers DNA etc. The future prospects of mechanical bottom up nanomanipulation for biomedical technology, food technology are discussed.
3D NANOMANIPULATION: DESIGN AND APPLICATIONS OF FUNCTIONAL NANOSTRUCTURED BIO-MATERIALS (Conference Paper)
(IOP Publishing Ltd, 2020) Lega, P V; Orlov, A P; Frolov, A V; Subramani, R; Irzhak, A V; Koledov, V V; Smolovich, A.M; Shelyakov, A.V
Recent progress in the development of the new functional materials opens up exciting possibilities for designing reconfigurable micro- and nano-structures and for operating mechanical nanotools which are controlled by external fields or heat. The nanotools such as nanotweezers with an active layer thickness of about several tenths of nm, and whose overall size is of the order of 1 μm can be applied to different micro- and nanoobjects. The present report gives an overview of the application of mechanical nanotools in 3D nanomanipulation of bio-nano objects such as micro biofibers DNA etc. The future prospects of mechanical bottom up nanomanipulation for biomedical technology, food technology are discussed.
A COMPREHENSIVE REVIEW OF LEARNING RULES AND ARCHITECTURE OF PERCEPTRON IN ARTIFICIAL NEURAL NETWORKS (ANNS) (Book Chapter)
(CRC Press, 2024-1) Shanthini S; Devi, M. Sindhana; Grace, R. Suriya
The complicated neural networks of the human mind have acted as a significant model for creating artificial neural networks (ANNs) of computational intelligence. ANNs can recognize patterns in data, make decisions, and perform other functions. The study provides a comprehensive review that explores ANNs by analysing the crucial elements of learning rules and perceptron architectures. This chapter clarifies the foundational learning rules underlying ANNs’ ability to adapt and generalize from data. The investigation comprehensively inspects the vital elements of learning rules and perceptron architectures in Artificial Neural Networks (ANNs) inspired by the detailed neuronal networks of the human brain. This chapter subsequently explores the dynamic realm of perceptron architectures within ANNs. Single-layer perceptrons are examined for their inability to handle intricate relationships. In contrast, multilayer perceptrons (MLPs) emerge as formidable solutions. The complex composition of MLPs, characterized by input, hidden, and output layers, is deconstructed, highlighting their potential to capture intricate non-linear patterns through the strategic deployment of activation functions. This analysis showcases a merging of academic notions and actionable effects. The combined effect between learning rules and perceptron architectures forms the foundation of ANNs’ expertise in pattern recognition, prediction, and decision-making tasks. By comprehensively understanding these underpinnings, researchers and practitioners can connect the potential of ANNs across diverse domains.
ACENAPHTHO[1,2-B]QUINOXALINE AND ACENAPHTHO[1,2-B]PYRAZINE AS CORROSION INHIBITORS FOR MILD STEEL IN ACID MEDIUM
(Elsevier, 2016-01) Saranya, J; Sounthari, P; Parameswari, K; Chitra, S
The corrosion inhibition of mild steel in 1 M H2SO4 using acenaphtho[1,2-b]quinoxaline and acenaphtho[1,2-b]pyrazine at 303–333 K have been investigated. The study was performed using weight loss method, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS). Polarization measurements proved that the inhibitors behave as mixed-type. EIS data showed that the charge transfer resistance of mild steel increases in acid solution containing inhibitors. The surface morphology was evaluated using scanning electron microscope (SEM), atomic force microscopy (AFM) techniques. Density functional theory (DFT) at the B3LYP/6-311G(d,p) basis set level was performed. Excellent correlation was found between experimental and theoretical results.
ACTIVATED CARBON FROM AGRICULTURAL WASTE AS AN ADSORBENT: ADSORPTION OF (R.ORANGE 3R) DYE FROM AQUEOUS SOLUTION (Abstract Only)
(Indian Journal of Environmental Protection, 2005-01) Sameena, Y; Thangmani, K S; Madhavakrishnan; Pattabhi, S
Activated carbon (AC) prepared from silk cotton hull was used to remove textile dye (R. Orange 3R) from aqueous solution by adsorption under different conditions, such as agitation time, adsorbent dosage, pH and dye concentration. The time required to attain equilibrium was found to be 60 min for all the concentrations studied (5 to 20 mg/L). Adsorption followed both Langmuir and Fruendlich isotherms. The present removal was decreased with increase in pH. The adsorption capacity was found to be 27.54 mg/g of AC at a pH of 2 ± 0.2 at room temperature (30 ± 2°C) for the particle size of 125-250μm. Adsorption capacity was depend on pH of the solution, adsorbent dosage and initial dye concentration.
ACTIVATED CARBON FROM INDUSTRIAL SOLID WASTE AS AN ADSORBENT FOR THE REMOVAL OF RHODAMINE-B FROM AQUEOUS SOLUTION: KINETIC AND EQUILIBRIUM STUDIES
(Elsevier, 2005-08) Kadirvelu, K; Karthika, C; Vennilamani, N; Pattabhi, S
The activated carbon was prepared using industrial solid waste called sago waste and physico-chemical properties of carbon were carried out to explore adsorption process. The effectiveness of carbon prepared from sago waste in adsorbing Rhodamine-B from aqueous solution has been studied as a function of agitation time, adsorbent dosage, initial dye concentration, pH and desorption. Adsorption equilibrium studies were carried out in order to optimize the experimental conditions. The adsorption of Rhodamine-B onto carbon followed second order kinetic model. Adsorption data were modeled using both Langmuir and Freundlich classical adsorption isotherms. The adsorption capacity Q0 was 16.12 mg g−1 at initial pH 5.7 for the particle size 125–250 μm. The equilibrium time was found to be 150 min for 10, 20 mg l−1 and 210 min for 30, 40 mg l−1 dye concentrations, respectively. A maximum removal of 91% was obtained at natural pH 5.7 for an adsorbent dose of 100 mg/50 ml of 10 mg l−1 dye concentration and 100% removal was obtained when the pH was increased to 7 for an adsorbent dose of 275 mg/50 ml of 20 mg l−1 dye concentration. Desorption studies were carried out in water medium by varying the pH from 2 to 10. Desorption studies were performed with dilute HCl and show that ion exchange is predominant dye adsorption mechanism. This adsorbent was found to be both effective and economically viable.
ACTIVE-POLYPHENOLIC-COMPOUNDS-RICH GREEN INHIBITOR FOR THE SURFACE PROTECTION OF LOW CARBON STEEL IN ACIDIC MEDIUM
(World Scientific Connecting Great Minds, 2020) Chung, I.-M; Hemapriya, V; Kanchana, P; Kim, S.-H; Prabakaran, M
Eco-friendly biodegradable Rhododendron schlippenbachii (R. schlippenbachii) green inhibitors, R. schlippenbachii methanolic (RSMeOH) extract, which can effectively reduce low carbon steel corrosion rate, were investigated using weight-loss and electrochemical (electrochemical impedance spectroscopy) techniques. The inhibitors exhibited higher efficiency by retarding the corrosion process in 1M H2SO4 and the inhibition efficiency is found to be concentration dependent. The reactivity of the predominant phytochemical components of the extract are analyzed. The adsorption of inhibitors on low carbon steel is followed the Langmuir adsorption. The protective inhibitor film formed on the metal surface was confirmed by SEM and AFM techniques.
ADDRESSING THE MICROPLASTIC POLLUTION: A SOCIETAL CHALLENGE (Article)
(Springer Nature, 2025-01) Udhayakumar, Minisha; Udhayakumar, Shanmugapriya; Pitchaimuthu, Subha Bharathi; Alagarsamy, Sandhya; Thirumalaisamy, Kayalvizhi; Azeem, Muhammad; Govindarajan, Ramkumar; Damodharan, Karthiyaini; Madhubala Parameswaran, Ayyappa Das; Arockiam Jeyasundar, Parimala Gnana Soundari
Microplastics (MPs) pollution in soil have emerged as a significant environmental concern, infiltrating ecosystems and posing threats to ecological, plants, human, and animal health. We aim to provide a comprehensive understanding of microplastics, exploring their types, sources, pathways, and impacts across different environmental compartments. Begins with an introduction to microplastics, this review offers details on their classification and examines their omnipresence in aquatic and across other environments highlighting their persistent nature and complex pathways. It culminates the urban runoff, industrial discharges, anthropogenic activities, and agricultural inputs as major contributors, underscoring the need for targeted intervention strategies. The review underscores the detrimental effects of microplastics on aquatic life, soil fertility, and food safety, while also addressing the broader societal implications, including economic costs and public health concerns. Sampling and detection methods for microplastics are critically reviewed, covering advanced techniques and technologies that enable accurate identification and quantification of these pollutants. Overall, underscoring the dynamic nature of the microplastic pollution by synthesizing current knowledge and advancements, this review calls for the long-term monitoring and adaptive management strategies for future research, policy-making, and public initiatives towards a sustainable and microplastic-free environment
ADSORPTION AND DENSITY FUNCTIONAL THEORY ON CORROSION OF MILD STEEL BY A QUINOXALINE DERIVATIVE
(Scholars Research Library, 2015) Saranya, J; Sounthari, P; Parameswari, K; Chitra, S
(3E)-3-{[4-(phenylsulfonyl)]imino}-3,4-dihydroquinoxalin-2(1H)-one (PSDQO) has been synthesized and its inhibiting action on the corrosion of mild steel in 1 M H2SO4 has been assessed by weight loss method at 303 K – 333 K. The results of the investigation show that this compound has excellent inhibiting properties for mild steel corrosion in sulphuric acid. Inhibition efficiency increases with increase in the concentration of the inhibitor. The adsorption of the inhibitor was tested for Langmuir, Temkin, Flory-Huggin’s and El-Awady isotherm and proved physical adsorption. Quantum chemical calculations were employed to give further insight into the mechanism of inhibitive action of the inhibitor
ADSORPTION AND INHIBITIVE PROPERTIES OF TRIAZOLOPYRIMIDINE DERIVATIVES IN ACID CORROSION OF MILD STEEL
(Hindawi, 2011) Parameswari, K; Chitra, S; Kavitha, S; Rajpriya, J; Selvaraj, A
Inhibitive and adsorption properties of synthesized triazolo- pryimidine derivatives (P1, P2 & P3 ) for the corrosion of mild steel was investigated using weight loss and electrochemical methods. Inhibition efficiency increased as the concentration of the inhibitor increased but decreased with increase in temperature. The triazolopyrimidines were found to act as adsorption inhibitors for the corrosion of mild steel. The adsorption mechanism of the triazolopyrimidine was found to be physisorption, spontaneous and exothermic. Also the adsorption followed Langmuir adsorption isotherm. polarisation studies showed that the inhibitors behave as cathodic type.
ADSORPTION AND INHIBITIVE PROPERTIES OF TRIAZOLOPYRIMIDINE DERIVATIVES IN ACID CORROSION OF MILD STEEL (Article)
(Hindawi, 2010-12-15) Parameswari, K; Chitra, S; Kavitha, S; Rajpriya, J; Selvaraj, A
Inhibitive and adsorption properties of synthesized triazolo- pryimidine derivatives (P1, P2 & P3 ) for the corrosion of mild steel was investigated using weight loss and electrochemical methods. Inhibition efficiency increased as the concentration of the inhibitor increased but decreased with increase in temperature. The triazolopyrimidines were found to act as adsorption inhibitors for the corrosion of mild steel. The adsorption mechanism of the triazolopyrimidine was found to be physisorption, spontaneous and exothermic. Also the adsorption followed Langmuir adsorption isotherm. polarisation studies showed that the inhibitors behave as cathodic type.
ADSORPTION BEHAVIOR OF VX NERVE AGENT ON X12Y12 NANOCAGES: A DENSITY FUNCTIONAL THEORY STUDY
(Springer, 2024-08) Prince Makarios Paul, S; Parimala devi, D; Praveena, G; Jeba Beula, R
Herein our study, analysis on the adsorption of VX nerve agent on to X12Y12(Al12N12, Al12P12, C12Si12 and Mg12O12) nanocages is done using density functional theory (DFT). All the calculations were performed using DFT/B3LYP-D3/6-31G (d) basis set, to delve into the capability of these nanocages for sensing and adsorption of VX. Various parameters such as adsorption energy (Eads), energies of highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), Fermi energy (EF), work function alteration (%∆Φ), energy gap (Eg), global electron density transfers (GEDT) along with molecular electrostatic potential (MEP) and density of states (DOS) profiles of the isolates and complex were calculated, compared and examined. The findings exhibited O atom of VX to interact with Al, Si and Mg atoms of the respective nanocages, and the nature of interaction was from nearly covalent to van der Waals. Furthermore, the potential for the nanocage to sense the target gas was analyzed by means of Fermi energy (EF), alteration in work function (%∆Φ) and its recovery time (τ). Among the considered nanostructures, Mg12O12 was recorded with the highest adsorption energy of−97.39 kcal/mol, suggesting it to be a promising adsorbent for VX.
ADSORPTION CHARACTERISTICS OF IOTA-CARRAGEENAN AND INULIN BIOPOLYMERS AS POTENTIAL CORROSION INHIBITORS AT MILD STEEL/SULPHURIC ACID INTERFACE
(Elsevier, 2017-04) Nirmala Devi, Gowraraju; Saranya, Jagadeesan; Kiruthika, Ayyasamy; Lukman O, Olasunkanmi; Eno E, Ebenso; Chitra, Subramani
The corrosion inhibition efficiency performance of biopolymers Iota-carrageenan (IC) and Inulin (INU) on mild steel in 0.5 M H2SO4 solution was evaluated using weight loss, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The inhibition efficiency of the inhibitors increased with increase in concentration. Thermodynamic parameters (ΔGads) and activation parameters (Ea, ΔHo, ΔSo) were calculated to investigate the mechanism of inhibition. Polarization studies revealed that the studied inhibitors are mixed type. Scanning electron microscope (SEM), energy dispersive X-ray spectroscopic (EDX) and atomic force microscopy (AFM) studies were used to characterize the surface morphology of inhibited and uninhibited mild steel.
ADSORPTION OF REACTIVE BLUE 171 FROM AQUEOUS SOLUTION USING LOW COST ACTIVATED CARBON PREPARED FROM AGRICULTURAL SOLID WASTE: ALBIZIA AMARA
(Applied Science Innovations Pvt. Ltd., India, 2015-08-25) Anitha, K; Syed Shabudeen, P S; Karthikeyan, S; Aruna devi, N
The adsorption of Reactive Blue 171 (Reactive Dye) from aqueous solution using activated carbon prepared from Albizia amara pod shell waste as an adsorbent have been carried out. The experimental adsorption data fitted reasonably well to Langmuir and Freundlich adsorption isotherms. Kinetic parameters as a function of Initial dye concentration have been calculated and the kinetic data were substituted in Pseudo First Order, Elovich and Pseudo Second order equations. A probable explanation is offered to account for the results of kinetic study. The thermodynamic parameter enthalpy change (∆H) suggests the exothermic nature of absorption of Reactive Blue 171 onto activated Albizia amara pod shell waste carbon.
ADVANCED DOMAIN ADAPTATION FOR SKIN DISEASE SEGMENTATION AND CLASSIFICATION USING BOOTSTRAPPING OF FINE-TUNED DEEP LEARNER
(Springer Link, 2023-09-29) Kalaivani, A; Karpagavalli, S
In medical diagnostic systems, the most challenging task is to segment and classify the varieties of skin disorders from dermoscopic images. For this purpose, Bootstrapping of Fine-tuned Segmentation and Classification Network (BF-SegClassNet) model was designed, which uses (i) cycle-Generative Adversarial Network (GAN) as domain adaptation, (ii) modified SegNet as segmentation and (iii) fine-tuned ResNet18 with Bootstrapping as classification. But, the efficiency of cycle-GAN was degraded if the source domain differs largely from the target domain. Hence, in this article, a Fuzzy Transfer Learning (FTL) model is developed based on fuzzy logic as domain adaptation. In this model, 2 different stages are performed such as training and adaptation. During the training stage, the source labeled data is used to build the Fuzzy Inference System (FIS), which extracts information from the source and transfers it to the target domain. The fuzzy sets and fuzzy rules created by an Adhoc Data-Driven Learning (ADDL) activity are included in the FIS. The created source FIS and the target data are used in the adaptation stage to adapt the fuzzy rule and the fuzzy rule base from the FIS to extract dissimilarities in the data and help bridge the contextual gap between the source and target. Thus, this FTL model is applied instead of cycleGAN to create more samples, which are further partitioned and classified by the BF-SegClassNet model efficiently. Finally, the testing outcomes exhibit that the FTL model attains a mean accuracy of 98.08% for the HAM dataset compared to the other GAN models.
ADVANCES IN FIELD EFFECT TRANSISTOR BASED ELECTRONIC DEVICES INTEGRATED WITH CMOS TECHNOLOGY FOR BIOSENSING (Review)
(Elsevier B.V., 2025) Rai, Harshita; Singh, Kshitij RB; Natarajan, Arunadevi; Pandey, Shyam S
This review article embarks on an enlightening journey through the multifaceted realm of electronic devices and their applications in biosensing, emphasizing the role of Field effect transistor (FET) based biosensors and Complementary Metal Oxide Semiconductor (CMOS) processes in biosensing device development. It begins by elucidating the foundational principles of biosensing and underscoring the crucial contribution of transducers, establishing a robust understanding of the field. The article unravels the intricate interplay between electronic biosensors and CMOS processes, offering a concise yet insightful exploration of their operational intricacies, diverse practical applications, and recent advancements. Additionally, it spotlights the pivotal role of FET-based biosensors integrated with CMOS processes in miniaturizing biosensors and thus amplifying their real-world efficacy. Moreover, the role of modern technologies, such as the Internet of Things (IoT), in recent biosensor development has been discussed. By addressing inherent challenges like sensitivity, integration, cost, and accessibility, the article underscores the vital role of biosensing technologies driven by electronic devices in wearable technology development. In addition, integrating these devices to fit with the ongoing trend of VLSI technology faces significant challenges. To overcome this aspect, sensors based on molecularly imprinted polymers (MIPs) can be the best alternative, as they will avoid utilizing bioreceptors, as it simplifies integration by reducing complexity, enhancing stability, and improving compatibility with CMOS processes. Hence, this review's distinct contribution lies in its comprehensive approach, shedding light on how biosensing technologies, underpinned by electronic devices such as FETs and CMOS processes, offer solutions for realizing modern-day devices.
AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN-LIGAND AND PROTEIN-PROTEIN INTERACTIONS WITH FUNCTIONAL DEEP LEARNING
(Blue Eyes Intelligence Engineering & Sciences Publication, 2019-06) Asha, P R; Vijaya, M S
Drug discovery of incomparable hereditary disorder like spinocerebellar ataxia is confronted and an enforce task in biomedical study. There are number of paths available for affinity prediction through scoring functions and ideals in the catalog. Nevertheless there is a need for artistic access in portraying the affinity of spinocerebellar ataxia which will facilitate enhanced prediction for drug discovery. This research work portrays the significance of docking for protein-ligand interaction and protein-protein interaction with modeling through deep learning. Deep Neural Networks is utilized in predicting binding affinity with 3d protein structures and ligand. Predictive models have been built with features related to for protein-ligand interaction and protein-protein interaction. In the first case, 17 protein structures and 18 ligands were used. Each protein structure is docked with ligand to get essential features like energy calculations, properties of protein and ligand for predicting binding affinity. In the next case, repeat mutation is induced manually with 17 protein structures and docked with 18 ligands. To train the model, well-defined descriptors are squeezed from the docked complex. Third case employs protein-protein interaction of total of 626 protein structures and the complexes attained from the protein-protein interaction are 313. Features like energy calculations, physio-chemical properties and interfacial and non-interfacial properties are extracted for learning this model. Deep learning has the property of representation learning from the user defined features, which helps in accurate prediction of binding affinity. The predictive models are developed with functional deep neural network and their performances are compared with sequential deep neural network. Functional deep neural network have more flexibility to define layers, complements sequential deep neural network which results in improved performance.
AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN-PROTEIN INTERACTIONS AND DEEP NEURAL NETWORK WITH USER-DEFINED LAYER
(International Journal of Advanced Science and Technology, 2019) Asha, P R; Vijaya, M S
Binding affinity prediction for a rare genetic disorder like spinocerebellar ataxia is crucial in biomedical study. Numerous models for affinity prediction have been developed through machine learning and deep learning. The basic deep neural network architecture uses a linear stack of layers and sharing of layers is not feasible whereas the functional deep neural network uses sharing of layers but the models are affected, when there is a change in layer. Hence complex models cannot be constructed and cannot predict binding affinity efficiently. This problem can be overcome by customizing the layers in deep neural network architecture. In this research work, the network layers are defined by sharing features with several layers and weights are trained and updated for every iteration to obtain accurate prediction. The work is implemented with 626 protein structures for protein-protein interaction and 313 complexes are attained from the protein-protein interaction. Binding site is identified by passing the 3D protein structures into convolutional neural network. Features like energy calculations, physio-chemical properties and interfacial and non-interfacial properties are extracted from interacted complex for building the model. Feature representations are learned automatically by deep learning through trainable weights in customized layers. Deep neural network with user defined layers is modelled with three optimizers and the results are correlated with functional deep neural network based affinity prediction models. The result shows that the proposed deep neural network with customized layers and adam optimizer achieves the highest prediction rate of 0.98.
AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA
(Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Asha, P R; Vijaya, M S
Drug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.