Research

Transition Metal Catalysis

We use an integrated computational–experimental approach to uncover the mechanisms behind transition metal catalysis, spanning carbene insertion chemistry and spin-state-driven iron catalysis.

Photocatalysis

Our work exploits photoexcitation to unlock reactivity inaccessible to ground-state chemistry, guiding the design of selective, light-driven catalytic transformations.

Key Areas of Interest

Mechanistic studies of enzyme catalysis, including factors influencing rate enhancement and selectivity
Metal–hydride hydrogen atom transfer (MHAT) processes in iron-dependent enzymatic systems
Reactivity and mechanisms in diiron enzyme catalysis
Stereodivergence in enzymes, where specific mutations (via directed evolution) enable access to multiple stereoisomers

To reveal the mechanistic underpinnings and to gain rational insights, we use state-of-the-art computational techniques, including DFT calculations, Docking, Molecular Dynamics (MD) simulations, QM cluster calculations, hybrid QM/MM approaches and electric field.

Orchestrating Metal–Hydride Reactivity in Engineered Metalloenzymes: Electronic and Electrostatic Determinants of Reaction Mechanism. R. Chatterjee*,G. Jindal*, J. Am. Chem. Soc. 2026, 148, 18383−18395.

QM/MM Calculations reveal the Molecular Origin of Stereodivergence in a CALB Catalyzed Transacylation. M.S. Harariya‡, R. Balhara‡, G. Jindal*, J. Chem. Inf. Model. 2026, 66, 5953−5963.

T he Electrostatic Edge: Decrypting the Near-Perfect Catalytic Efficiency of Fumarase. R. Chatterjee‡, R. Balhara‡, G. Jindal, ACS. Catal. 2025, 15, 1739-1752.

Reaction Outcome Prediction
Using descriptor-based ML models to correlate computed molecular descriptors with experimental yields and selectivities — moving from post-hoc rationalization toward predictive models.

LLMs and Generative AI for Chemistry
Exploring how large language models and generative AI can assist in reaction design, literature mining, and hypothesis generation at the interface of computational and experimental chemistry.

Automated Pipelines
Developing the automated pipeline to encode and preserve axial chirality in transition-metal complexes, enabling machine learning-ready datasets for asymmetric catalysis — a gap no existing cheminformatics tool addresses.
Overcoming Axial‑Chirality Bottleneck in Automated Intermediate Generation for Machine‑Learning Applications. A. Tyagi, M. Joddar, G. Jindal*, ChemRxiv. 2026. Preprint.