about

I'm Dhruv Menon, an applied Machine Learning Scientist and Research Engineer building generative AI systems for scientific discovery. My work combines foundation models, chemical language models, representation learning, flow-based generation, and physics-aware simulation workflows to navigate chemical design spaces and accelerate scientific discovery.

I'm completing my PhD (thesis submitted: Oct 2026) in Chemical Engineering at the University of Cambridge supervised by Prof. David Fairen-Jimenez. I build generative AI systems for the discovery & design of inorganic-organic materials called metal-organic frameworks for applications ranging from drug delivery to carbon capture & water harvesting. I also have interests in mechanistic underpinnings of performance, which I resolve using molecular simulations and statistical mechanics.

In 2025, I undertook a research fellowship at UC Berkeley with Prof. Omar Yaghi (Nobel Laureate, 2025), where I worked on generative materials discovery and automated laboratories. I earned my Master's by research (M.Res) in Physical Sciences (2023) from the Cavendish Laboratory, University of Cambridge and my Bachelors of Technology in Materials Sciences (2022) from the Indian Institute of Technology Gandhinagar.

Let's navigate the latent space together.move to explore · click to sample

interests

I have eclectic research interests, however, the central `theme' that connects everything is computation, rational design and accelerated scientific discovery. My interests include (in no particular order): de novo molecular design, language models, diffusion models, flow models, small-molecule discovery, protein design, metal-organic frameworks & supramolecular materials, physisorptive carbon capture, gas storage & separations, and drug delivery.

On a philosophical note, I'm NOT interested in incremental research (atleast not anymore). I tend to focus on moonshots, and would like to surround myself with people with the same mission & ambition.

research

Ongoing & recent

generative design

Inverse design of metal-organic frameworks

Developed Nexerra, a generative molecular design framework combining transformer-based chemical language models, variational autoencoders, and conditional flow matching for inverse molecular design. Developed representation learning methods for molecular embeddings, latent-space optimization, and conditional molecular generation under chemical validity, synthesizability, and physical design constraints. Implemented objective steering for end-to-end AI-guided materials discovery for adsorption, gas storage, water harvesting, and drug delivery.

Source: Menon et al., JACS, 2026

protein dynamics

Predicting protein conformations

Developing all-atom equivariant neural networks to accelerate molecular dynamics simulations and improve sampling of protein conformational landscapes. Exploring trajectory-learning approaches and protein foundation-model embeddings for predicting and generating alternative protein conformations.

Source: work in progress

drug delivery

Computational drug delivery & nanomedicine

Developed interpretable machine learning models to predict the biocompatibility of drug delivery systems, enabling high-throughput screening of ~100,000 experimentally reported materials for nanomedicine applications. Built a hierarchical virtual screening workflow to identify optimal drug carriers for chemotherapeutics. Discovered PCN-222 as a promising drug delivery platform, which was subsequently synthesized and tested in mouse models of pancreatic cancer, where the optimized formulation demonstrated reduced tumour growth and metastatic spread.

Source: Melle, Menon et al., Advanced Materials, 2024

proton transport

Confined proton transport

Developed multiscale simulation workflows integrating Grand Canonical Monte Carlo and molecular dynamics to investigate molecular transport, and proton-conduction mechanisms in functional porous materials. Studied diffusion, radial distribution functions, hydrogen-bond networks, velocity correlations, spatial distributions, and transport behaviour. Integrated molecular simulations with structural and experimental evidence to identify the mechanisms governing transport under nanoconfinement.

Source: Zhao, Li, Menon, Zhu et al., Nature Chemistry, 2026

papers

20+ peer-reviewed publications across materials chemistry, molecular simulation, and AI-guided materials design, including Nature Chemistry, Advanced Materials, Chem, Matter, and Chemical Society Reviews. 900+ citations; h-index 16. Full list on . ϕ denotes co-first authorship.

1. 2026

   generative design

   Flow-Guided Chemical Language Modeling for Linker Design in Reticular Chemistry

   Menon, D., Singh, V., Chen, X., Alizadeh Kiapi, M.R., Zyuzin, I., MacLeod, H.W., Rampal, N., Shepard, W., Yaghi, O.M. and Fairen-Jimenez, D. · Journal of the American Chemical Society, 148(37), pp.39546-39555

2. 2026

   proton transport

   Cation-directed assembly and sequential functionalization enable superprotonic polyanion-organic frameworks for high-power fuel cells

   Zhao, Q.^ϕ, Li, B.^ϕ, Menon, D.^ϕ, Zhu, C.^ϕ, Alizadeh Kiapi, M.R., Chen, X., Long, D.-L., Liu, J., Xiao, Z., Yang, D., Fairen-Jimenez, D., Zang, H.-Y., and Xuan, W. · Nature Chemistry

3. 2025

   ML screening

   Guiding the rational design of biocompatible metal-organic frameworks for drug delivery

   Menon, D. and Fairen-Jimenez, D. · Matter

4. 2024

   drug delivery

   Rational Design of Metal-Organic Frameworks for Pancreatic Cancer Therapy: From Machine Learning Screening to In Vivo Efficacy

   Melle, F., Menon, D., Conniot, J., Ostolaza-Paraiso, J., Mercado, S., Oliveira, J., Chen, X., Mendes, B.B., Conde, J., and Fairen-Jimenez, D. · Advanced Materials, p.2412757

5. 2024

   gas separation

   Flexibility-frustrated porosity for enhanced selective CO₂ adsorption in an ultramicroporous metal-organic framework

   Chen, X.^ϕ, Menon, D.^ϕ, Wang, X., He, M., Alizadeh Kiapi, M.R., Asgari, M., Lyu, Y., Tang, X., Keenan, L.L., Shepard, W., Wee, L.H., Yang, S., Farha, O.K., and Fairen-Jimenez, D. · Chem

The PhenoMenon

Essays & shortposts on AI, science & life.

1. Soon

   shortpost

   Large language models & the illusion of competence

   Dhruv Menon

background

1. 2023–26

   phd

   PhD in Chemical Engineering

   University of Cambridge · Supervisor: Prof. David Fairen-Jimenez

   Thesis: Computational Design of Metal-Organic Frameworks, submitted October 2026

2. 2025

   fellowship

   Winton Cambridge–Berkeley Exchange Fellowship

   UC Berkeley · With Prof. Omar Yaghi

   Generative design of metal-organic framework (MOF) water harvesters

3. 2022–23

   m.res

   M.Res in Physical Sciences

   University of Cambridge · EPSRC CDT in Nanoscience and Nanotechnology (NanoDTC)

   Rotations with Prof. Jeremy Baumberg (nanophotonics) and Prof. Erwin Reisner (adaptive evolution of syngas-fermenting bacteria)

4. 2020

   fellowship

   Summer Undergraduate Research Fellowship

   California Institute of Technology, remote due to COVID-19 · With Prof. Austin Minnich

   Reducing electronic noise in semiconductors

5. 2018–22

   b.tech

   B.Tech in Materials Science

   Indian Institute of Technology Gandhinagar

   Honours: Institute Gold Medal; Director’s Silver Medal; Gold Medal for Outstanding Research; Dean’s List; Academic Excellence Scholarships. Institute football team.