http://repository.iiitd.edu.in/xmlui/handle/123456789/1804 Year-2025 Sun, 20 Sep 2026 14:40:46 GMT 2026-09-20T14:40:46Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2057 Function prediction of protein cavities using 3D shape descriptors and machine learning Mittal, Pratham; Ray, Arjun (Advisor) Understanding protein function at the structural level requires accurate representation of their biologically active forms. However, structures deposited in the Protein Data Bank (PDB) often include only the asymmetric unit from crystallographic experiments, leading to discrepancies with the true oligomeric state. Such inconsistencies introduce significant challenges in down- stream analyses, particularly in cavity detection, where inter-subunit voids may be misidentified as functional binding sites. This work aims to address these challenges by constructing a curated dataset of protein struc- tures that accurately represent their functional oligomeric assemblies. PDB files were prepro- cessed to eliminate structural artifacts and to reconstruct biologically relevant assemblies. The cleaned structures were then analyzed using the CICLOP tool to identify cavity-lining residues, from which geometric and residue-level features were extracted. These features will serve as the foundation for machine learning models aimed at predicting protein function based on cavity characteristics. By integrating structural data correction with robust cavity analysis, this research establishes a reliable and interpretable pipeline for protein function prediction, with potential applications in structural bioinformatics and drug discovery. Fri, 18 Jul 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2057 2025-07-18T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2056 Embedding morality in AI systems Goel, Rahul; Mutharaju, Raghava (Advisor) The increasing integration of Artificial Intelligence into complex societal domains necessitates a deeper understanding of how to embed ethical reasoning into machines. This project addresses the challenge of quantifying and predicting alignment with established human ethical frameworks. Our methodology involved three key stages. First, we established a granular theoretical framework by defining 15 distinct ethical theories under the three major schools of thought: Consequentialism (α), Deontology (β), and Virtue Ethics (γ). Second, we developed a novel dataset of 450 ethical scenarios, specifically designed to elicit responses corresponding to each of the 15 theories. These scenarios were then annotated using Large Language Models (LLMs) to generate quantitative scores (α, β, γ) representing the relevance of each ethical school to a given case. Finally, we benchmarked a suite of classical machine learning models to predict these ethical alignments from textual features. Two primary experiments were conducted: a multi-output regression task to predict the (α, β, γ) scores and a multi-output classification task to predict the specific ethical school and theory. In the regression task, Linear Regression demonstrated the strongest explanatory power, achieving an R-squared (R2 ) value of 0.5804. For classification, Logistic Regression was the top- performing model, achieving an Exact Match Accuracy of 74.44% and 100% accuracy in identifying the high-level ethical school. This work provides a foundational dataset, a robust evaluation of classical machine learning models for ethical prediction, and confirms that quantitative representations of ethical schools are viable targets for computational modeling. The findings lay the groundwork for future research in building more transparent, auditable, and ethically-aligned AI systems. Fri, 18 Jul 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2056 2025-07-18T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2039 FMT hub phase II: comprehensive web infrastructure for the FMT center and the microbiome informatics lab at IIIT-Delhi Bhaskar, Siddharth; Bali, Siddhant; Kumar, Rishabh; Ghosh, Tarini Shankar (Advisor) We present FMT Hub - Phase II, We are developing the official website for the Microbiome Informatics Lab at IIIT Delhi as a digital extension of the FMT Hub. The platform is being built uscentralized 15, React 19, Node.js, Tailwind CSS, and TypeScript, focusing on modular UI, SEO, and responsiveness. We’re implementing secure APIs, dynamic routing, and scalable backend architecture for seamless performance. Deployment is handled on Oracle Linux with Apache, using SSL-secured HTTPS. This site will serve as a centralized digital gateway for lab research, data resources, and collaborations. Thu, 17 Jul 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2039 2025-07-17T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2038 Unsupervised clustering of kinases based on sequence and structural features Yadav, Karan; Singh, Manveet; Gautam, Mayank; Ray, Arjun (Advisor) The way kinases interact with their substrates has been a longstanding problem in biology prediction and important to drug developing. This report is about a new computational system like Multimodal Siamese Graph Network that is helpful in predicting the ability of kinase substrate engagement effectively, using different data modalities. Our model is on both sequence and structural based features. The structural information in PDB files has been mapped into a graphical form and analyzed by structural features (binding pockets and secondary structures) along with sequence information as pre-processed structural information (including ESM-2 protein language model embeddings). Siamese network architecture is learning to differentiate among interacting pairs, and non-interacting pairs using a triplet Margin Loss syntax. On a held out validation set our model achieves a state of the art Area Under the Precision-Recall Curve (AUPRC) and Area Under the Receiver Operating Characteristic (AUROC) of 0.785 and 0.786, respectively, far outpacing the baseline models, including Random Forest and XGBoost. Impressively, the model is unsupervised in that the kinases in their existing biological families are clustered which implies that the network is learning biologically important representations. The application proves not only the effectiveness of the combination of deep sequence, structural information to model molecular recognition but also represents a solid foundation towards the future in silico discoveries of the phosphorylation phenomena by means of generative models Thu, 24 Jul 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2038 2025-07-24T00:00:00Z