Year-2024Year-2024http://repository.iiitd.edu.in/xmlui/handle/123456789/17212026-08-30T16:16:20Z2026-08-30T16:16:20ZDevelopment of a haptic device for surgical training in mixed realityDharmenderParihar, RiteshShankhwar, Kalpana (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/20342026-08-26T22:00:38Z2024-11-27T00:00:00ZDevelopment of a haptic device for surgical training in mixed reality
Dharmender; Parihar, Ritesh; Shankhwar, Kalpana (Advisor)
This project focuses on designing a haptic device integrated into a Virtual reality
(VR) environment to enhance surgical training. Combining force feedback, sensor
integration, and 3D modeling, the device offers a realistic simulation of surgical
procedures. Using Unity for simulation, Blender for 3D modeling, and precise
sensors for feedback, the system aims to bridge the gap between theoretical
learning and practical expertise. The innovation lies in its immersive, interactive
design and its potential for reducing errors in surgical training.
2024-11-27T00:00:00ZPredicting lipid-binding proteins using structural biology and ML approachesSingh, Kumar AryanSaini, KartikeyaRay, Arjun (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/20332026-08-26T22:00:40Z2024-11-26T00:00:00ZPredicting lipid-binding proteins using structural biology and ML approaches
Singh, Kumar Aryan; Saini, Kartikeya; Ray, Arjun (Advisor)
Predicting lipid-binding proteins is essential for understanding their roles in cellular functions and disease mechanisms. This research aims to develop a machine learning (ML) model to accurately identify lipid-binding proteins by leveraging structural biology data. Data was sourced from the RCSB Protein Data Bank (PDB), UniProt, and various research publications, resulting in an extensive database with features including UniProt IDs, structural angles, molecular dimensions, sequence lengths, and lipid-binding categories. The dataset was meticulously cleaned by removing duplicates and verified for binding sites using PDB files through keyword searches and structural analysis. Comprehensive mapping of proteins to their respective lipid types was achieved by reviewing research articles and utilizing specialized databases. This curated dataset lays the groundwork for building an ML model, which is expected to enhance the precision of lipid-binding protein predictions. The anticipated model will facilitate advancements in drug discovery and deepen our understanding of lipid-related diseases, demonstrating the significant potential of integrating structural biology with computational approaches in biomedical research.
2024-11-26T00:00:00ZPredicting allostery in proteinsVirdi, Mandeep SinghVerma, Lovleen KGupta, ArnavRay, Arjun (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/20322026-08-26T22:00:25Z2024-11-29T00:00:00ZPredicting allostery in proteins
Virdi, Mandeep Singh; Verma, Lovleen K; Gupta, Arnav; Ray, Arjun (Advisor)
Allosteric site prediction has emerged as a pivotal approach in drug discovery, offering enhanced selectivity and reduced side effects compared to traditional orthosteric targeting. This report explores multiple computational strategies for identifying allosteric sites, with a focus on G protein-coupled receptors (GPCRs). Among the surveyed methods, machine learning models like PASSerRank demonstrate exceptional performance by employing ensemble learning, automated optimization, and ranking algorithms to prioritize potential allosteric sites with high accuracy. The solution implemented integrates structural and sequence filtering, feature ex- traction, and advanced predictive modeling, achieving robust results by leveraging techniques such as PDB data curation, fpocket-based pocket detection, and cheminformatics-driven feature analysis. Our workflow highlights the importance of integrating machine learning with domain- specific constraints to refine predictions and pave the way for the discovery of novel therapeutic targets.
2024-11-29T00:00:00ZLarge language model in bio-medical domainGupta, BhavyaSengupta, Debarka (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/20312026-08-26T22:00:33Z2024-12-13T00:00:00ZLarge language model in bio-medical domain
Gupta, Bhavya; Sengupta, Debarka (Advisor)
The rapid advancement of biomedical research has led to an overwhelming influx of scholarly articles, making efficient retrieval and comprehension of domain-specific knowledge a significant challenge. This project presents a novel implementation of a Retrieval-Augmented Generation (RAG)-based chatbot designed specifically for the biomedical domain. Unlike existing systems, this chatbot dynamically processes user-provided keywords in real-time, fetches relevant research papers from sources such as ArXiv, and generates contextually accurate explanations using domain-specific models like BioBERT. The chatbot eliminates the need for pre-uploading documents to an LLM interface by leveraging advanced retrieval techniques and transformer-based natural language models. Instead, it autonomously retrieves, processes, and summarises relevant information, significantly reducing time and effort. This innovation holds immense significance for researchers, clinicians, and educators by providing a seamless interface for accessing and understanding biomedical literature across various subfields.
2024-12-13T00:00:00Z