Science

Science: Exploration of the Chemical Space

Illuminating the Dark Molecules of Life

The Ahuja Laboratory at IIIT-Delhi is dedicated to pioneering the systematic exploration of the vast, uncharted chemical space of life. The central research mission is the functional elucidation of metabolites—the crucial yet understudied "dark molecules" of biology.

Research Philosophy: Beyond the Central Dogma

Moving beyond the classical flow of genetic information, research at the lab focuses on understanding how the metabolome dynamically interacts with and regulates all layers of cellular machinery—genomics, transcriptomics, proteomics, and epigenomics.

Metabolites are now recognized as potent bioactive molecules that directly influence phenotype through diverse mechanisms, including:

Orthosteric and allosteric modulation of proteins
Epigenetic modification (e.g., as substrates for histone/DNA modifiers)
Regulation of protein aggregation and phase separation
Direct influence on cellular differentiation and fate

Despite their significance, the non-canonical, regulatory functions of over 99% of known metabolites remain obscure. This critical knowledge gap defines the "dark molecules" the lab seeks to illuminate.

An Interdisciplinary Methodology

To address the structural and functional diversity of metabolites, the laboratory employs a convergent, systems-level approach, integrating:

Computational & AI/ML Frameworks: Including de novo ligand design, graph neural networks (GNNs), knowledge graphs, and multimodal deep learning.
Functional Genetics: Utilizing high-throughput yeast genetics and engineered biosensing platforms.
Molecular & Cell Biology: Biochemical assays, omics profiling, and high-content phenotypic screening.
Systems Biology: Integrative analysis to construct predictive models of metabolic regulation.

This methodology enables a pipeline from in silico prediction to in vivo and in vitro validation.

Key Research Themes & Projects

1. Metabolites as Endogenous Carcinogens: Predicting Oncometabolites

The lab developed Metabokiller, a computational framework that identifies metabolites with carcinogenic potential (oncometabolites) by evaluating bioactivity signatures. Current work focuses on next-generation models that incorporate endogenous metabolic context and provide mechanistic, explainable predictions.

2. Metabolites as Endogenous Allosteric GPCR Modulators

Research in this area investigates intracellular GPCR modulation by endogenous metabolites. Using Gcoupler, an AI-driven platform, the laboratory identifies metabolic modulators of GPCR signaling. Discoveries in yeast models are being translated to human GPCRs to investigate pathophysiological relevance.

3. Metabolites as Cell-Cell Communicators

This theme explores the hypothesis that tumor-specific metabolites and ectopically expressed odorant receptors (ORs) facilitate communication within the tumor microenvironment. The lab employs AI-based prediction tools (OdoriFy, EvOlf) and high-throughput microfluidics assays to validate this signaling paradigm.

4. Hunting Geroprotective Metabolites

An AI-powered workflow has been constructed to screen metabolomes for metabolites with geroprotective properties. This pipeline predicts pro-longevity molecules, infers their molecular targets, and facilitates validation in model systems to uncover endogenous regulators of longevity.

5. A Multimodal Prediction Engine for Cellular Aging

The laboratory developed scCamAge, a context-aware, multimodal deep learning engine that integrates single-cell spatiotemporal features and morphometrics to predict cellular age. Demonstrating evolutionary conservation, the tool predicts senescence across species. Efforts are ongoing to enhance its generalizability with human clinical data.

Expanding the Frontier: Additional Research Directions

The laboratory's research portfolio includes several complementary investigations:

Metabolic Regulation of DNA Repair: Examining how central metabolic pathways influence the DNA damage response.
Multitarget Metabolites & Protein-Protein Interactions: Deciphering metabolites that modulate protein complexes.
Metabolism and the Epigenetic Landscape: Elucidating how metabolic flux provides substrates that sculpt the epigenome.
Knowledge Graphs for Synthetic Biology: Building AI-powered systems to design efficient microbial cell factories.

The Ahuja Laboratory is charting the unknown territories of the biochemical universe, transforming dark molecules into beacons of biological understanding and therapeutic innovation.

For specific publications, software tools, and collaboration inquiries, please explore the subsequent pages or contact the laboratory directly.

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