• Learning from high dimensional healthcare data to improve interpretability and insights 

  Jha, Indra Prakash; Kumar, Vibhor (Advisor) (IIIT-Delhi, 2023-05)

  The escalating volume and intricate nature of healthcare and social care datasets necessitate the implementation of unconventional feature learning strategies to tackle current challenges. The examination of health and ...
• Computational design of multi-targeting lead-compounds to disrupt quorum sensing pathway for combating drug-resistant gramnegative bacteria superbugs 

  Yadav, Dilip Kumar; Murugan, N Arul (Advisor); Sharma, D K (Advisor) (IIIT-Delhi, 2023-12)
• Integrated large-scale analysis of disease signatures in oral microbiomes 

  Shete, Omprakash; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2024-08)

  Oral health is essential for overall human well-being, with oral diseases impacting 3.5 billion people worldwide every year (WHO Report 2022). Oral microbiome is considered as the 2nd most abundant human body site with ...
• AgAnt : a computational tool to assess the mode of interaction between a protein-ligand complex 

  Choudhary, Bhumika; Ray, Arjun (Advisor) (IIIT-Delhi, 2024-08)

  Protein-ligand interactions are fundamental in regulating protein activity within cellular environments, playing a pivotal role in numerous biochemical processes. These interaction studies are important for understanding ...
• A deep learning approach for predicting antimicrobial resistance across various bacterial species using whole genome sequence 

  Muddemmanavar, Prateeksha; Sengupta, Debarka (Advisor) (IIIT-Delhi, 2024-05-01)

  Antimicrobial resistance (AMR) presents an immediate threat to public health as microorganisms evolve to resist antimicrobial drugs, leading to challenging or untreatable infections. AMR-related costs could exceed 1 trillion ...
• Deep learning based molecule generation for developing novel therapeutics for neurodegenerative diseases 

  Joshi, Piyush; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2024-05-01)

  Deep learning models and generative AI have revolutionized drug discovery, especially with regard to neurodegenerative diseases like Alzheimer's. The developing of canonical SMILES (Simplified Molecular Input Line Entry ...
• Evaluation of state-space models and widely used architectures in deep learning for heterogenous tasks 

  Paul, Ankush; Kumar, Vibhor (Advisor) (IIIT-Delhi, 2024-08-01)
• Novel framework for predicting multitarget antifungal drugs against pathogenic fungi 

  S, Karthika; Ahuja, Gaurav (Advisor) (IIIT-Delhi, 2024-06)

  Pathogenic fungal diseases have become a global threat to human health in immunocompromised individuals. With increasing mortality rates due to systemic infections and limited availability of antifungal classes, it is ...
• Identifying novel caloric restriction mimetics using generative AI 

  Nanshi, Jeet; Ahuja, Gaurav (Advisor) (IIIT-Delhi, 2024-01-20)

  In the contemporary era, anti-aging stands as a prominent research focus. Recognizing the impact of diet on aging, investigations into caloric restriction methods have emerged as a promising and effective approach in ...
• Machine learning and tanimoto similarity based virtual screening of chemical libraries to develop novel PD therapeutics 

  Singhal, Divyanshi; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-05-01)

  Parkinson's disease is a prevalent neurodegenerative disorder that affects a significant portion of the aging population worldwide. The etiology of Parkinson's disease involves the accumulation of misfolded proteins, ...
• Developing machine learning and deep learning models for predicting the thermal stability of proteins 

  Singh, Surabhi; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-05-01)

  The accurate prediction of protein stability temperatures is essential for numerous applications in bioinformatics and biotechnology. In this study, we utilized a multifaceted computational approach to develop predictive ...
• Insights into the mechanisms of resistance to axitinib in cancer cell lines 

  Patni, Khyati; Dhanjal, Jaspreet Kaur (Advisor) (IIIT-Delhi, 2024-06)

  Along with the remarkable success stories in the evolutionary journey of targeted cancer treatment, there are examples where the targeted therapies failed to match the expectations. There have been multiple reports of ...
• Deciphering the molecular complexity of dyslipidemia : multi-omics meta-analysis of the gut microbiome and the host physiology 

  Verma, Shivangi; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2024-08-01)

  The gut microbiome is intricately linked with various aspects of health and physiology, serving as both a potential diagnostic marker and therapeutic target. Understanding its mechanistic in􀏐luences on systemic processes, ...
• Machine learning and deep learning models for prediction of protein-ligand binding affinity 

  Kaur, Parneet; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-05-01)

  In recent years, there has been significant interest in using Machine Learning and Deep Learning to predict protein-ligand binding affinity. This is due to the rapid growth of the computational approaches that have evolved ...
• Characterization of non-antibiotic medication associated microbiome module in the human gut microbiome 

  Regonda, Vindhya; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2024-05-18)

  Pharmacomicrobiomics, an emerging field at the intersection of microbiology and pharmacology, explores the intricate relationship between drug response and the gut microbiome, offering new avenues for precision medicine. ...
• Machine learning based design of multi-targeting inhibitors for Alzheimer disease and structure based validation 

  Kumar, Virendra; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-06-01)

  Neurodegenerative diseases are one of the major problems in today's world, and Alzheimer's is one of them. The disease-associated symptoms are not diagnosed earlier, and also very few FDA-approved disease-reversing drugs ...
• Development of the scoring function for protein ligand binding affinity by implementing graph-theory based approaches 

  Singh, Shikha; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-05-01)

  Scoring functions are essential to computational drug discovery since they evaluate or predict a ligand's binding affinity to its target protein. These functions rank the goodness of fit of a ligand into the binding site ...
• Developing machine learning and deep learning models for predicting the folding rate of proteins and peptides 

  Tyagi, Reegina; Murugan, N Arul (Advisor) (IIIT-Delhi, 2024-05-21)

  In order to comprehend the functionality and stability of proteins and peptides, it is essential to forecast their folding rates. This thesis explores the construction of sophisticated machine learning (ML) and deep learning ...
• Unveiling E. coli adaptation dynamics with protein language model and random walk 

  Jaiswal, Nancy; Sengupta, Debarkar (Advisor) (IIIT-Delhi, 2024-05-21)

  Biological processes rely on intricate interactions among multiple genes, forming diverse networks like protein-protein interaction networks, gene regulation networks, gene co-expression networks, and metabolic networks. ...
• Data-driven exploration of cross-body site interactions between oral and gut microbiome 

  Mahajan, Sakshi; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2024-05-20)

  This study investigates the link between the oral and gut microbiome, two highly diverse microbial communities with established connections to human health. We per formed meta-analysis on across those studies containing ...

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