http://repository.iiitd.edu.in/xmlui/handle/123456789/1244 2026-08-24T08:03:12Z 2026-08-24T08:03:12Z Jain, Shipra Raghava, Gajendra Pal Singh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1999 2026-08-19T22:00:25Z 2025-11-01T00:00:00Z

Development of biomolecule-based in-silico models for cancer diagnostics and therapeutics Jain, Shipra; Raghava, Gajendra Pal Singh (Advisor) Cancer remains a major global health challenge, necessitating advanced computational resources to support effective treatment and disease management. These resources can assist researchers in the early detection of cancer and the screening of therapeutic molecules. This study presents a suite of machine learning-based in-silico models and resources developed to facilitate cancer diagnosis and therapy. First, we developed a highly accurate classifier based on a ten-gene panel selected using propensity indices. This model reliably distinguishes prostate cancer from normal samples, offering a potential non-invasive alternative to conventional diagnostic procedures. In recent years, there has been a notable shift in drug discovery from small molecules to peptide-based therapies. To support this transition, we developed THPdb2, an updated repository of FDA-approved therapeutic peptides. Building on this, we introduced THPPred, a machine learning-based tool that predicts novel druggable peptides by analysing their physicochemical, molecular, and sequence features, thereby streamlining therapeutic screening. To further support peptide-based immunotherapy, we developed IL13Pred, a predictive model that identifies IL-13-inducing peptides, aiding in the design of cytokine-based cancer immunotherapies. Additionally, for small-molecule drug discovery, we created NfκBin, an in-silico model that predicts NF-κB inhibitors using molecular fingerprints, accelerating the identification of compounds targeting this key oncogenic pathway. Collectively, these integrated models and databases form a comprehensive computational pipeline for cancer biomarker discovery and therapeutic screening. By combining gene expression analysis, peptide-based drug prediction, and small-molecule screening, this framework advances personalized diagnostics and treatment strategies in oncology. Mostly resources developed in this study are publicly accessible via a web-based platform and GitHub.

2025-11-01T00:00:00Z Singh, Pradeep Sethi, Tavpritesh (Advisor) Lodha, Rakesh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1995 2026-08-17T22:00:39Z 2026-05-01T00:00:00Z

SAFE-ICU: advancing AI-driven decision support in the intensive care units Singh, Pradeep; Sethi, Tavpritesh (Advisor); Lodha, Rakesh (Advisor) Integrating artificial intelligence into healthcare presents an unprecedented opportunity to address critical challenges in Intensive Care Units. The objective of this thesis is to create, validate, and deploy AI models to increase safety through the development of early warning systems for drug-drug interactions and predicting clinical decompensation of patients. Our first objective is creation of a data lake involving multimodal data, which combines vital signs, laboratory tests, treatment histories, patient files, and thermographic videos; not just images. The combination of structured and unstructured data contained in the datasets offers unmatched insight into intensive care unit patients, thereby enhancing the overall quality of research. The datasets used for prediction and diagnostics are pre-processed through various techniques like feature extraction, temporal alignment, etc. The second Objective aims at the creation of actionable, predictive, and management tools for sophisticated critical care of ICU patients. For example, ThermalShockNet, which was created for pediatric ICU settings and utilizes thermal videos and patient vitals to identify subtle physiological changes indicative of hemodynamic shock. This model uses data from thermal frames and vital signs to make predictions. When combined using deep learning, these predictions can reveal the onset of shock before it appears in a patient. ThermoGnose, a pipeline developed specifically for the early prediction of hypothermia, uses non-linear features drawn from patient monitoring data to estimate hypothermia onset and provide vital lead time for clinical intervention. SIgnose predicts shock risk by analyzing vital sign trends. When combined, this variety of approaches highlight how AI can address complex challenges in the ICU. The third Objective was to improve drug safety in the ICU. For this, the ContraIndicator tool was created. NLP processes the treatment chart and line listing of drugs to find probable adverse drug–drug interactions. The ContraIndicator insights assist clinicians in minimizing the chance of adverse reactions. The end Objective was SafeICU, a modular and explainable AI-driven real-time platform for the pediatric ICU. It brings together early warning systems along with safety and infection surveillance modules, which include vital signs, thermal imaging, medication, and microbiology results. At SafeICU, they help flag unusual Shock Index values in many ICUs. The ContraIndicator is on the lookout for drug interactions through the examination of ICU prescriptions. AMRtrack monitors the emergence of antimicrobial resistance by processing laboratory reports, mapping trend data, and informing infection control planning. When these modules are together, real-time alerts, risk scores, and infection monitoring are delivered into the ICU workflow. Overall, this thesis provides a clinically oriented framework for using AI in ICUs. It shows how models that can be multimodal, explainable, and deployable work together in real time to assist clinical decision making in high-acuity clinical settings. These advances create a pathway to the secure and scalable exploitation of AI in critical care and, eventually, precision medicine in health care that are rich in data.

2026-05-01T00:00:00Z Arora, Sakshi Ahuja, Gaurav (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1964 2026-04-29T22:00:10Z 2026-03-01T00:00:00Z

Mechanism-informed, AI-driven frameworks for discovery and validation of aging-associated chemical space Arora, Sakshi; Ahuja, Gaurav (Advisor) Aging is a progressive, multifactorial biological process that drives the risk of nearly all major chronic diseases, including cancer, neurodegeneration, metabolic disorders, frailty, and cardiovascular dysfunction. Although the past two decades have established a molecular framework through the Hallmarks of Aging, translating this knowledge into actionable, small-molecule interventions that enhance healthspan remains a central challenge in the field of geroscience. Experimental discovery pipelines are slow, resource-intensive, and typically explore only a minute fraction of chemical space. Conversely, computational drug discovery approaches, while high-throughput, often rely on chemistry-centric descriptors, exhibit black-box behavior, lack mechanistic interpretability, and rarely generalize to biologically novel molecules. This thesis addresses these long-standing limitations by developing two complementary artificial intelligence systems, AgeXtend and AgeXtend:: Mimetics, designed to accelerate mechanism-informed discovery of geroprotective molecules and caloric restriction mimetics (CRMs). The first objective introduces AgeXtend, a multimodal, bioactivity-driven, and fully explainable AI framework. AgeXtend integrates curated datasets of experimentally validated geroprotectors and neutral compounds with bioactivity-based descriptors, hallmark-specific classification models, toxicity prediction, and target inference modules. By combining mechanistic knowledge with machine learning, AgeXtend achieves robust predictive accuracy across cross-validation, leave-one-out validation, and independent external datasets. Importantly, the explainability module maps predictions onto nine aging pathways, allowing for a mechanistic interpretation of each compound’s mode of action. Large-scale screening of ~1.1 billion compounds yielded diverse chemical classes with strong geroprotective potential. Experimental validation confirmed these predictions across three biological systems: Saccharomyces cerevisiae chronological lifespan assays, human fibroblast senescence assays, and Celegans lifespan assays. Endogenous metabolites and repurposed drugs predicted by AgeXtend demonstrated lifespan-extending or senomodulatory activity, underscoring the biological fidelity of its predictions. Building upon this foundation, the second objective presents AgeXtend::Mimetics, a novel computational framework designed to identify Caloric Restriction Mimetics, compounds capable of reproducing CR-like physiological responses without structural similarity to known CRMs. Unlike existing approaches that rely on transcriptomic signatures alone or structural matching, AgeXtend::Mimetics explicitly decouples biological convergence from chemical divergence. Using dual similarity modeling, ridge regression residuals, supervised contrastive learning, and composite CRM fingerprinting, the framework identifies molecules whose biological signatures align strongly with known CRMs despite having distinct chemical architectures. Large-scale application across thousands of compounds revealed chemically novel, mechanistically plausible CRM candidates that align with pathways such as nutrient sensing, autophagy, mitochondrial remodeling, and metabolic regulation. This framework substantially broadens the chemical landscape of CRM discovery and provides mechanistic clarity on CRM-like effects. Together, the approaches developed in this thesis demonstrate that explainable, mechanism-oriented AI models can successfully bridge the gap between large-scale chemical exploration and biological relevance. AgeXtend and AgeXtend::Mimetics collectively advance the field of computational geroscience by enabling scalable, interpretable, and experimentally validated discovery of geroprotectors and CRMs. These contributions lay the groundwork for future translational studies, the development of generative design for longevity therapeutics, and the integration of multi-omic datasets to refine mechanism-based discovery pipelines. The thesis highlights both the promise and current limitations of AI in aging biology, providing a roadmap for next-generation computational frameworks that target healthspan extension.

2026-03-01T00:00:00Z Tomer, Ritu Raghava, Gajendra Pal Singh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1834 2026-04-03T22:00:27Z 2025-10-01T00:00:00Z

In silico approaches for biomolecule-driven disease diagnosis and therapy Tomer, Ritu; Raghava, Gajendra Pal Singh (Advisor) Developments in computational biology have facilitated the systematic study of biomolecules including peptides, proteins or nucleic acids. Such advancements have provided disease-oriented research and therapeutic discovery opportunities. The thesis deals with two broad disciplines in this field: Disease Diagnosis and Biomolecule-Based Therapeutics, with a particular focus on integrating curated data resources and machine-learning-based predictive systems. The initial part is aimed at enhancing the molecular knowledge and diagnostic studies of mucormycosis, a serious and rapidly spreading fungal infestation also referred to as “Black Fungus”. Here, we have a developed a web-repository titled as “MucormyDB”. The repository is a compilation of genomic, proteomic, virulence and therapeutic information. This repository enables researchers to easily perform comparative analyses and allows the identification of possible genetic biomarkers. With such capabilities MucormyDB is a valuable resource to study the molecular basis of mucormycosis. The second part of the thesis provides computational models for peptide-based therapeutics prediction. This involves IL4pred2, a machine-learning model that predicts peptides that can induce interleukin-4, and the AntiCP4, which predicts anticancer peptides with enhanced predictive capability. In addition to therapeutic prediction, identification of safety of peptide candidates is also taken into account in the thesis. In order to deal with this aspect, we have designed RAIpred to detect peptides that could trigger rheumatoid arthritis. We have also developed CDpred to predict peptides related to the celiac disease. These tests provide a valuable adjunct for immunological risk determination. They help researchers to select safer peptide candidates in the early phase of drug discovery early. Together, the resources developed in this thesis present high-quality molecular data, powerful predictive algorithms, and easily available computing platforms that can be used to study mucormycosis and complementarily improve the systematic evaluation of therapeutic peptides and the potential immunological consequences of peptides.

2025-10-01T00:00:00Z