• Benchmarking fold recovery: implicit-solvent atomistic & coarse grained simulations of two vs non two state proteins 

  Das, Mimansha; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2025-08)

  Protein folding is a fundamental biological process through which a polypeptide adopts its functional three-dimensional structure. In this study, we systematically investigated the structural response of proteins to thermal ...
• Challenges in the prediction of half-life of lncRNAs 

  Shuhail, Shaikh; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2026-01)

  Long non-coding RNAs (lncRNAs) are key regulators of gene expression, and their stability, commonly quantified as half-life, plays a critical role in cellular function. Recent computational efforts have attempted to predict ...
• Role of cofactors/co-enzymes in stabilizing protein-ligand complexes 

  Anand, Harnoor Kaur; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2025-08)

  Flavin adenine dinucleotide (FAD) is a ubiquitous redox cofactor that plays a fundamental role in various metabolic and enzymatic reactions. It is involved in electron transfer processes essential for cellular respiration, ...
• Targeting neutrophil elastase in alpha-1 antitrypsin deficiency: a GenAI-based approach for peptide inhibitor discovery 

  Paul, Prateek; Dhanjal, Jaspreet Kaur (Advisor) (IIIT-Delhi, 2025-08)

  Alpha-1 Antitrypsin Deficiency (AATD) is a hereditary disorder primarily affecting the lungs and liver, often resulting in early-onset chronic obstructive pulmonary disease (COPD) due to the unchecked activity of Neutrophil ...
• Advancing gene signature discovery with generative models: a case study ovarian cancer 

  Anand, Alok; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2025-06)

  Ovarian cancer is a highly aggressive malignancy with poor survival rates, largely due to late detection and extensive tumor heterogeneity. This study introduces a computational framework, NIGAM (Normalize, Identify, ...
• Graph-based deep learning for pIC50 estimation in cancer drug screening 

  Jadhav, Rushikesh Sanjay; Dhanjal, Jaspreet Kaur (Advisor) (IIIT-Delhi, 2025-06)

  Accurately predicting drug efficacy remains a significant challenge in the drug discovery process. One of the key pharmacological metrics used to assess the potency of a compound is the half-maximal inhibitory concentration ...
• An improved method for predicting conformational B-Cell epitopes in a protein sequence from its primary structure 

  Pandey, Anupma; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2025-06)

  The original CBTOPE method, introduced in 2009, was one of the first approaches for predicting conformational B-cell epitopes using only protein sequence information. While it has been widely adopted in the scientific ...
• A comprehensive compilation of anticancer peptides and prediction of anticancer activity in chemically modified peptides 

  Chauhan, Milind; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2025-06)

  This thesis presents a repository CancerPPD2, which is an updated version of CancerPPD, developed to maintain comprehensive information about An-ticancer peptides and proteins. It contains 6521 entries, each entry provides ...
• Deep learning-based image-to-SMILES conversion of 2D chemical structures 

  Alisha; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2025-07)

  Chemical information science faces an important bottleneck because millions of chemical structures are trapped in visual formats throughout scientific literature and patents, making them inaccessible for automatic analysis ...
• LLM-assisted ontology mapping for semantic interoperability in structured biomedical data 

  S, Prasanna Kumar; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2025-06)

  The exponential growth of biomedical data promises new insights, but semantic heterogeneity and inconsistent metadata limit reuse. In practice, many publicly available datasets (e.g., tabular datasets from Figshare or ...
• Comparing the performance of quantum descriptors and classical descriptors based ML models for drug like property prediction 

  Riya; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2025-08)

  In drug discovery, the accurate prediction of pharmacokinetic properties plays a crucial role in identifying compounds with favorable absorption, distribution, metabolism, and excretion profiles. Among these, the partition ...
• Machine learning provides preliminary snapshots of disease-specific variations in gut microbial aging-trajectories 

  T R, Seerapthynath; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2025-08)

  The human gut microbiome plays a critical role in metabolism, immunity, and disease resistance, with its composition shifting notably with age. These changes have positioned the microbiome as a potential “biological clock” ...
• Deep learning based protein thermal stability prediction 

  Sharma, Umang; Murugan, N. Arul (Advisor) (IIIT-Delhi, 2025-07)

  The thermal stability of a protein is a fundamental biophysical property that governs its structural integrity, functional efficacy, and practical utility, particularly its shelf-life in therapeutic and industrial applications. ...
• Gene regulatory network inference by reducing dimensions for single-cell transcriptome profiles 

  Meshram, Ankur Gajendra; Kumar, Vibhor (Advisor) (IIIT-Delhi, 2025-05)

  Understanding the complex regulatory relationships between genes is central to conducting cellular functionality and disease. However, the inference of Gene Regulatory Networks (GRNs) from sparse single-cell RNA sequencing ...
• Machine learning regression models for predicting hemolytic concentration (HC50) of peptides using curated activity data 

  Singh, Ayushi; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2025-06)

  In this thesis, we first compiled hemolytic activity data of peptides in terms of hemolytic concentration (HC50), defined as the concentration required to lyse 50% of red blood cells (RBCs). We then developed regression ...
• Challenges in understanding cell-cell communication for tumor microenvironment using spatial transcriptomics 

  K V, Gowtham; Kumar, Vibhor (Advisor) (IIIT-Delhi, 2025-08)

  Traditional RNA sequencing methods, such as bulk RNA-seq, fail to capture cellular heterogeneity. In contrast, single-cell RNA-seq addresses this limitation by profiling individual cells, but it loses spatial context. ...
• Mechanistic insights into the hepatoprotective activity of terminalia arjuna 

  Tiwari, Deepshikha; Dhanjal, Jaspreet Kaur (Advisor) (IIIT-Delhi, 2025-05)

  Non‐alcoholic fatty liver disease (NAFLD) affects roughly one quarter of the world’s population and is tightly associated with obesity and metabolic syndrome; despite its high prevalence, no fully effective pharmacotherapies ...
• Multipurpose denoising autoencoder based framework for understanding taxa specific states in the gut microbiome 

  Khare, Shruti; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2025-06)

  Microbiome data is often challenged by the fact that the obtained microbiome compositional profile may not be either the true or the ideal one. There is a problem certain that species may not be detected because of being ...
• Compilation and prediction of hemolytic peptides using machine learning techniques 

  S A, Kavin Raj; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2025-06)

  This thesis describes the compilation, characterization, and prediction of hemolytic peptides, which are responsible for lysing red blood cells. We present Hemolytik2, a comprehensive repository that significantly updates ...
• AntiCP3: prediction of anticancer proteins using evolutionary information from protein language models 

  Gupta, Amisha; Raghava, Gajendra Pal Singh (Advisor) (IIIT-Delhi, 2025-05)

  Several computational tools have been developed to predict the anticancer nature of peptides, including AntiCP and AntiCP2. While these methods have been widely adopted by the scientific community, they are not suitable ...

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