http://dspace.dtu.ac.in:8080/jspui/handle/123456789/40 2026-04-28T04:03:45Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/21835 Title: EVALUATION OF PHYTOCHEMICALS FROM TINOSPORA CORDIFOLIA AGAINST ONCOGENIC AND RESISTANCE-ASSOCIATED TARGETS IN LOW-GRADE SEROUS OVARIAN CARCINOMA Authors: SINGH, PRATYAKSHA Abstract: Low-grade serous ovarian carcinoma (LGSC) is not frequent but chemo-resistant subtype of epithelial ovarian cancer. Key features of this particular subtype are slow proliferation and poor response to conventional chemotherapy treatment. This study aims to identify plant-derived compounds that are capable of modulating key target involved in oncogenic and resistance pathways in LGSC. Here, the focus was on six molecular targets KRAS, BRAF, MEK1 are the oncogenic drivers of LGSC whereas as HES1, EGFR, PIK3CA are involved in drug-resistance. Phytochemicals from Tinospora cordifolia (Giloy) which is a traditional medicinal plant belonging to the Menispermaceae family has been gaining significant attention recently due to its known anticancer properties. These phytocompounds were explored using a computational approach. Through literature mining a total of eighteen phytocompounds were shortlisted and seven with favourable ADME and drug-likeness profiles (DL score ≥ 0.5) were selected for further study. These shortlisted candidates were taken for molecular docking using AutoDock Vina using Pyrx, where Luteolin, Berberine, and Dehydrodiscretamine and Magnoflorine showed strong binding with key LGSC targets (>8.5 kcal/mol). All the promising ligand–protein complexes were then further analysed through molecular dynamics (MD) simulations using Desmond and Google Colab. Out of the tested protein-ligand complexes, good structural stability and a consisitent binding throughout the course of simulation was onserved in case of Luteoli-BRAF, Berbine-EGFR and Dehydrodiscretamine BRAF complexes. The PI3KCA-Berberine complex however showed moderate stability. MM/GBSA binding energy analysis further supported their binding affinity and complex stability. Overall, Luteolin, Berberine, and Dehydrodiscretamine emerged as the strongest multitarget inhibitors among all the tested phytochemicals. These compounds have potential to disrupt oncogenic signalling and overcome chemoresistance in LGSC. These findings provide a computational framework and lay the foundation for future experimental validation and therapeutic development using Tinospora cordifolia phytochemicals. 2025-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/21834 Title: COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR PLANT DISEASE DETECTION Authors: SAHOO, ELESWETA Abstract: Diseases in plants remain a serious hazard to the world’s food supply, farming results and environmental protection, especially in farming-reliant areas. Diagnosing plant diseases using old methods mostly involves looking at plants and this can be slow, subjective and easily incorrect. Because more people are being born, there is more demand for food which makes crop health and lower losses to diseases extremely important. Using pesticides as pest controls endangers health and damage to the environment and the growing resistance of pathogens has weakened their effectiveness. So, fast and accurate methods for detecting plant diseases are urgently needed. In modern developments in AI, ML and DL, it is now possible to use computers to identify plant diseases through digital image processing. SVM, RF, DT and GB are promising at classifying plants from their leaf images. These simple models depend on color, texture and shape brought out by HOG and LBP algorithms. However, although these methods are easy to use and explain, they need domain specialists to create features by hand and often fail with complex visual patterns. CNNs and similar models deliver results regardless of visual differences, since they can discover needed patterns right from the unprocessed images. Examples of these architectures such as VGGNet, ResNet, AlexNet and EfficientNet, have proven better at detecting and naming many plant diseases on datasets like Plant Village. With these models, it’s possible to reuse networks already trained without having much data in your domain. The use of flipping, rotation and brightness adjustment makes models work better and helps them avoid overlearning. In this study different models of DL & ML approaches are tested and assessed for infected plant detection using the Plant Village data in this research. The Objective was to discover the model that best and generally recognized plant leaf diseases through image data. Test accuracy was highest with 99% for the Random Forest classifier and was followed by the DT with 96% and GB with 95%. While the training accuracy for the SVM was high at 96%, its test accuracy fell to just 85%. As part of DL, popular training models were transferred and used in the field of deep learning. ResNet50 performed the best, reaching 96.8% test accuracy, while VGG16 had 95.2% and AlexNet came in at 94.1%. They worked well even with new, unseen data, as a result of using extra data and fine-tuning. Generally, the findings indicated that Ensemble Classifiers and CNN-based CNNs offer the best accuracy when using ML and DL, respectively. Using both DL and ML together will play a major role in the future of agriculture. As a result, they help to automatically recognize and identify plant diseases, aiding the practice of precision farming. Thanks to new developments in data gathering, understanding models and running them on phones, these technologies will greatly help with real time monitoring and crop health care. 2025-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/21832 Title: SYNERGIZING PHYTOCHEMICAL PHARMACOLOGY AND PRECISION GENE EDITING FOR MULTIMODAL AUTISM SPECTRUM DISORDER INTERVENTION Authors: BHATT, YOGITA Abstract: Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition that arises from complex interplay between genetic and metabolic dysfunctions. Addressing such a disorder demands a holistic and integrative scientific approach that bridges molecular genetics and pharmacological research. This study explores a dual-pronged therapeutic strategy aimed at correcting ASD-related abnormalities. The first approach utilizes CRISPR-Cas9 gene editing to target known ASD-associated mutations in critical synaptic genes. Simultaneously, the second strategy focuses on the bioactive potential of phytochemicals derived from Cichorium intybus, a plant known for its regulatory effects on metabolic enzymes that influence neuronal energy dynamics. Using advanced bioinformatic platforms and gene design tools, guide RNAs were meticulously engineered to minimize off-target effects while effectively modulating genes involved in metabolic control. These metabolic targets plays central role in maintaining the energy balance and neuronal communication within synaptic networks. One key compound, chlorogenic acid, emerged as a promising agent capable of restoring metabolic equilibrium while enhancing impaired neuronal signaling, suggesting a dual mechanism of action. Together, these gene and phytochemical interventions propose an innovative therapeutic framework: gene-edited cellular models can serve as testing grounds for evaluating the neuroprotective effects of these natural compounds. By aligning targeted genetic repair with plant-based metabolic modulation, this approach holds potential for enhancing neuronal viability and functional recovery. Ultimately, such integrative and personalized methodologies pave the way for the development of refined, indication-specific treatments for ASD, uniting precision medicine with nature-inspired solutions. 2025-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/21828 Title: INTEGRATIVE COMPUTATIONAL FRAMEWORK FOR DOPAMINE D3 RECEPTOR-TARGETED DRUG DISCOVERY: BRIDGING GENETIC VARIABILITY, STRUCTURAL DYNAMICS, AND MACHINE LEARNING Authors: HATWAL, AKSHAY Abstract: Aim: The Dopamine D3 receptor (D3R), a G-protein coupled receptor predominantly expressed in the limbic system, plays a pivotal role in modulating reward, cognition, and emotional behaviors, and is implicated in neuropsychiatric and neurodegenerative disorders such as Parkinson’s disease and schizophrenia. Given its therapeutic relevance, this study presents an integrated computational framework to elucidate the structural and functional consequences of D3R mutations, their impact on ligand binding, and the application of advanced machine learning for drug discovery. Evolutionary conservation analysis using ConSurf identified functionally critical regions within D3R, while PredictSNP predicted deleterious effects for 73 out of 405 single amino acid variants, with a majority located in highly conserved regions. To further probe the dynamic consequences of mutation, molecular dynamics (MD) simulations were performed for the Wild-type and selected variants (P344T, L60P) over 100 ns. A cascade neural network-based quantitative structure-activity relationship (QSAR) model was developed and trained on a large, chemically diverse dataset. The model utilized a two-stage architecture with uncertainty estimation and active learning, leveraging molecular fingerprints and 2D/3D descriptors. Performance evaluation demonstrated robust classification of high- and low-affinity ligands, with high ROC-AUC (0.888), average precision (0.916), and strong rank correlation (Spearman 0.780). The model’s precision-recall and ROC curves indicated high discriminative power, while confusion matrix analysis revealed a conservative bias, minimizing false positives at the cost of some false negatives-a trade-off often desirable in early-stage drug discovery. Results and Conclusion: The study identified 73 deleterious D3R variants, predominantly in conserved regions, correlating with reduced dopamine binding affinity and altered receptor dynamics. Molecular docking and MD simulations confirmed that mutations in critical regions impair function, while those in variable regions are generally tolerated. The cascade neural network QSAR model achieved high accuracy (79.7%), precision (91.6%), and ROC-AUC (0.888), effectively distinguishing high- and low-affinity ligands. Misclassifications were mainly confined to borderline cases, indicating robust model calibration. This integrative computational approach provides a powerful platform for D3R-targeted drug discovery, supporting the rational design and prioritization of novel therapeutics for neuropsychiatric and neurodegenerative disorders, with future work focusing on model interpretability and experimental validation. 2025-05-01T00:00:00Z