I am a molecular biologist with academic, industry and entrepreneurial experience — and CelerisAI sits at the precise intersection of those three careers.
My doctoral research at the University of Bath established the mechanistic basis of Bach-1 (and Nrf2)-mediated HO-1 gene regulation. My postdoctoral work at Yale School of Medicine then took me deeper into one of biology's most consequential puzzles: how hypoxia and ischemia-reoxygenation trigger catastrophic lung damage and systemic hyperinflammation. We mapped the pathways, identified the molecular switches, and demonstrated protective interventions. In the laboratory, we could reliably protect lung cells and animals from hypoxia-driven lung injury.
Then COVID-19 arrived — and changed everything.
As the pandemic swept the world, I watched with a particular and painful clarity. The virus, I understood immediately, was simply a trigger. The real mechanism of death was hypoxia-mediated hyperinflammation and lung damage — the precise biology I had spent my postdoc studying. Academic scientists across the globe, my colleagues and I among them, had already established the mechanisms and identified potential solutions. The knowledge existed in published papers. The interventions had worked in animal models.
But millions of patients died anyway — not because the science failed, but because it never left the laboratory. The gap between translational research and clinical medicine had claimed lives at pandemic scale. Novel innovations, painstakingly discovered by scientists around the world, remained where they had always been: in journals, unread by the patients who needed them most.
I could save lung cells and lab mice from hypoxia. I could not save humans during COVID. That gap — between what science knows and what medicine can do — is why CelerisAI exists.
— Dr. Chintan Raval, Founder & CEOThat experience is the founding conviction behind CelerisAI. Today, using advanced AI/ML tools, we are designing novel small molecules for hypoxia-mediated hyperinflammation across a range of lung diseases — COPD, pulmonary arterial hypertension, fibrotic disease, and ischemic injury. Every program is built on experimental evidence. Every target traces back to research I designed, ran, and published.
As Head of R&D and Quality at a US biotech company, I subsequently led multiple collagen-based instant hemostats and advanced wound healing products from concept through CDSCO regulatory approvals, commercialising two collagen hemostat products. I have also co-founded a regenerative medicine company, where I raised capital and built a manufacturing facility from the ground up, and an agricultural technology company. The path from bench to market is not theoretical to me — I have walked it.
Most AI drug discovery platforms begin with the algorithm and search for the biology. We begin with the biology and deploy the algorithm where it creates the most leverage: generative molecule design, ADMET and PK/PD prediction, target mining, and clinical outcome modelling. The result is a pipeline where every program is anchored in experimental evidence, every molecule is designed with commercial intent, and every target has active pharma deal flow to validate it.
We are not chasing a trend in AI. We are applying the best available tools to a set of biological problems I have spent my career understanding.
The .6 billion cost and 90% failure rate of traditional drug discovery are not inevitable. They are the product of guesswork applied at scale. CelerisAI replaces the guesswork — with biology first, AI second, and a clear path to pharma partnership at every stage.
I founded CelerisAI in Bengaluru to build the kind of company I wished had existed when I was a postdoc at Yale: one where deep science moves at the speed that AI now makes possible — and where discoveries made in the laboratory finally reach the patients who need them.
If you are an investor, a pharma/biotech partner, or an academic scientist who shares that conviction — I would like to hear from you.