Our Research Interest
KEy RESEARCH FOCUS AREAS
- Our lab's core mission is to decode the "language of chemicals." We operate on the principle that a molecule's structure contains vast, predictive information about its biological function. Our goal is to translate that structural information into a deeper understanding of health and disease.
- What differentiates our approach is the integration of chemical and biological data. Instead of analyzing chemical structures in isolation, we build models that represent molecules within a combined "bioactivity-chemical" space. This allows us to contextualize a chemical's properties with its known biological effects, leading to models with superior predictive power and real-world biological relevance.
- To build these sophisticated models, we enrich them with decades of established biological knowledge, integrating multi-omics data such as genes, mutations, and pathways. We develop and apply novel computational methods rooted in artificial intelligence, multimodal data integration, and emerging Large Language Model (LLM) architectures.
Research Areas
AI in Biology and Medicine
Cheminformatics & Drug Discovery
AI in Biology and Medicine
Developing predictive and explainable AI models to solve critical biomedical challenges, from understanding disease mechanisms to identifying novel diagnostic signatures.
Biology of Aging
Cheminformatics & Drug Discovery
AI in Biology and Medicine
Using our integrated models to understand the molecular drivers of aging and to discover novel geroprotectors-compounds capable of extending healthspan.
Cheminformatics & Drug Discovery
Cheminformatics & Drug Discovery
Cheminformatics & Drug Discovery
Creating new computational frameworks to predict a molecule's function, (e.g., carcinogenicity, therapeutic potential, or odor) directly from its structure, accelerating the discovery of new drugs and functional chemicals.
Teaching Philosophy
BIO545-Biostatistics (Elective)
This course serves as a gateway to quantitative biology. It equips students with the essential statistical reasoning and practical programming skills needed to design experiments, analyze complex biological data, and rigorously interpret results. We move beyond theory to tackle real-world case studies from genomics, clinical research, and systems biology, providing the foundational toolkit for any student in data-driven life sciences.
BIO2GMB-Genetics and Molecular Biology (Core)
This foundational course explores the "operating system" of life. We delve into the mechanisms of heredity, gene expression, and genome regulation. Understanding this core biological "language"- from DNA replication to the complex genetic pathways involved in disease, is essential for students who will go on to build the next generation of computational models to understand health, drug action, and evolution.
Probability and Statistics (PG Diploma)
This course is tailored for postgraduate students in Data Science & AI (PGDDSAI) and Digital & Sustainable HCS (PGD-DSHCS). It provides the essential mathematical framework for building, understanding, and critically evaluating modern machine learning and AI models. We focus on probabilistic reasoning and statistical inference, the bedrock upon which all data-driven discovery—from computational biology to digital health—is built.
TEAM MEMbers
Aayushi Mittal
Sanjay Kumar Mohanty
Vishakha Gautam
Graduate Student interested in developing AI models to find alternative functions of cellular metabolites.
Vishakha Gautam
Sanjay Kumar Mohanty
Vishakha Gautam
Graduate Student interested in developing Computer Vision-based AI modeling for predicting cellular aging.
Sanjay Kumar Mohanty
Sanjay Kumar Mohanty
Sanjay Kumar Mohanty
Graduate Student intrested in delineating the complexities of protein ligand interactions using Deep Learning approaches
Sakshi Arora
Saveena Solanki
Sanjay Kumar Mohanty
Graduate Student interested in understanding the chemical basis of longevity responses, with a special interest in using large language models.
Saveena Solanki
Saveena Solanki
Saveena Solanki
Graduate Student interested in understanding the multivalent-multi-targeting potential of cellular metabolities.
Subhadeep Duari
Saveena Solanki
Saveena Solanki
Graduate Student interested in developing novel models in the field of computer vision using Deep Learning
Shiva Satija
Suvendu Kumar
Sonam Chauhan
Graduate Student interested in the genotoxic research using a combination of next-generation sequencing (Nanopore) and generative AI-based computational methods
Sonam Chauhan
Suvendu Kumar
Sonam Chauhan
Graduate Student interested in the exploration of the Dark Matter of the Chemical World.
Suvendu Kumar
Suvendu Kumar
Suvendu Kumar
Graduate Student interested in developing novel computational methods for the better representation of the chemical compounds for the downstream Machine Learning Applications.
Arushi Sharma
Arushi Sharma
Suvendu Kumar
Graduate Student interested in development of sophisticated methods involving knowledge graphs and large scale language models.
SELECTED PUBLICATIONS
Protocol for cellular age prediction in yeast and human single cells using transfer learning. STAR Protoc. 2025 Sep 19;6(3):104023. doi: 10.1016/j.xpro.2025.104023. Epub 2025 Aug 11. PMID: 40794494; PMCID: PMC12357082.
Duari S, Gautam V, Ahuja G.
scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics. Cell Rep. 2025 Feb 6;44(2):115270.
Gautam V, Duari S, Solanki S, Gupta M, Mittal A, Arora S, Aggarwal A, Sharma AK, Tyagi S, Pankajbhai RK, Sharma A, Chauhan S, Satija S, Kumar S, Mohanty SK, Tayal J, Dixit NK, Sengupta D, Mehta A, Ahuja G.
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend. Nat Aging. 2024 Dec 3.
Arora S, Mittal A, Duari S, Chauhan S, Dixit NK, Mohanty SK, Sharma A, Solanki S, Sharma AK, Gautam V, Gahlot PS, Satija S, Nanshi J, Kapoor N, Cb L, Sengupta D, Mehrotra P, Ghosh TS, Ahuja G.
Advancing chemical carcinogenicity prediction modeling: opportunities and challenges.Trends Pharmacol. Sci. 44, 400–410 (2023)
Mittal, A. & Ahuja, G.
mRNA translational specialization by RBPMS presets the competence for cardiac commitment in hESCs. Sci Adv 9, eade1792 (2023)
Bartsch, D., Kalamkar, K., Ahuja, G., Lackmann, J.-W., Hescheler, J., Weber, T., Bazzi, H., Clamer, M., Mendjan, S., Papantonis, A. & Kurian, L.
Artificial intelligence uncovers carcinogenic human metabolites. Nat. Chem. Biol. (2022).
Mittal, A., Mohanty, S. K., Gautam, V., Arora, S., Saproo, S., Gupta, R., Sivakumar, R., Garg, P., Aggarwal, A., Raghavachary, P., Dixit, N. K., Singh, V. P., Mehta, A., Tayal, J., Naidu, S., Sengupta, D. & Ahuja, G.
OdoriFy: A conglomerate of Artificial Intelligence-driven prediction engines for olfactory decoding.J. Biol. Chem. 100956 (2021).
Gupta, R., Mittal, A., Agrawal, V., Gupta, S., Gupta, K., Jain, R. R., Garg, P., Mohanty, S. K., Sogani, R., Chhabra, H. S., Gautam, V., Mishra, T., Sengupta, D. & Ahuja, G.
Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research. Bioinformatics. 2021 Jul 19;37(12):1769-1771.
Gupta A, Choudhary M, Mohanty SK, Mittal A, Gupta K, Arya A, Kumar S, Katyayan N, Dixit NK, Kalra S, Goel M, Sahni M, Singhal V, Mishra T, Sengupta D, Ahuja G.
Loss of genomic integrity induced by lysosphingolipid imbalance drives ageing in the heart. EMBO Rep. 20, (2019).
Ahuja, G., Bartsch, D., Yao, W., Geissen, S., Frank, S., Aguirre, A., Russ, N., Messling, J.-E., Dodzian, J., Lagerborg, K. A., Vargas, N. E., Muck, J. S., Brodesser, S., Baldus, S., Sachinidis, A., Hescheler, J., Dieterich, C., Trifunovic, A., Papantonis, A., Petrascheck, M., Klinke, A., Jain, M., Valenzano, D. R. & Kurian, L.
yylncT Defines a Class of Divergently Transcribed lncRNAs and Safeguards the T-mediated Mesodermal Commitment of Human PSCs. Cell Stem Cell. 2019 Feb 7;24(2):318-327.e8. doi: 10.1016/j.stem.2018.11.005. Epub 2018 Dec 13. PMID: 30554961.
Frank, S*., Ahuja, G*., Bartsch, D., Russ, N., Yao, W., Kuo, J. C.-C., Derks, J.-P., Akhade, V. S., Kargapolova, Y., Georgomanolis, T., Messling, J.-E., Gramm, M., Brant, L., Rehimi, R., Vargas, N. E., Kuroczik, A., Yang, T.-P., Sahito, R. G. A., Franzen, J., Hescheler, J., Sachinidis, A., Peifer, M., Rada-Iglesias, A., Kanduri, M., Costa, I. G., Kanduri, C., Papantonis, A. & Kurian, L.
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High-affinity olfactory receptor for the death-associated odor cadaverine. Proc. Natl. Acad. Sci. U. S. A. 110, 19579–19584 (2013).
Hussain, A*., Saraiva, L. R*., Ferrero, D. M*., Ahuja, G*., Krishna, V. S., Liberles, S. D. & Korsching, S.
About The Ahuja Lab
AgeXtend
single cell camage
single cell camage
AgeXtend: AI-driven discovery and analysis of geroprotectors for healthy aging.
single cell camage
single cell camage
single cell camage
scCamAge: A deep-learning platform for comprehensive single-cell aging analysis, integrating image-based spatiotemporal features with cellular morphometrics and bioactivities to predict longevity and senescence across species.
Gcoupler
single cell camage
metabokiller
Gcoupler: An AI-driven toolkit for discovering endogenous GPCR modulators.
metabokiller
single cell camage
metabokiller
Metabokiller: An interpretable AI-driven classifier for accurate carcinogen prediction, integrating multiple cellular stress responses and outperforming existing methods, with experimental validation in yeast and human cells.
odorify
DeepGraphh
A web server with deep learning models for predicting odorant-odorant receptor interactions, identifying responsive receptors for odorants, classifying chemicals as odorants, and explaining predictions at the molecular level.
DeepGraphh
DeepGraphh
A user-friendly web service for graph-based QSAR analysis, offering multiple established methods for generating predictive models for classification and regression tasks.
ectracker
A user-friendly web server for single-cell data analysis, enabling identification of cell types, ectopically expressed genes, and their regulatory networks through quantitative and qualitative assessments.
ectracker
For more projects please refer to our Laboratory GitHub Page