Our Research Interest
KEy RESEARCH FOCUS AREAS
- Research Focus: Systems Biology, Synthetic Biology, and Chemical Biology, with a focus on understanding how cells respond to internal and external factors.
- Methodology: Combines Artificial Intelligence (Deep Learning, Natural Language Processing, Graph Theory) and experimental Molecular Biology (Yeast-based assays, Microfluidics).
- Key Projects:
- Decoding logics of cellular responses using AI and yeast sensors. Includes development of a target-drug database and a deep learning model for drug-target interaction.
- Linking metabolites to cell-cell communication using protein language models and cheminformatics.
- Deciphering interactions between chemical and biological systems using graph theory and yeast assays.
- Defining the "grammar" of induced cellular behavior using microfluidics-based assays.
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
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.
Loss of genomic integrity induced by lysosphingolipid imbalance drives ageing in the heart. EMBO Rep. 20, (2019).
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