The Invisible Cost of Drug Discovery

Every medicine that reaches patients carries an invisible history. Years - often decades - of scientific effort that never appear on a label. For every approved drug, thousands of ideas fail along the way: compounds that looked promising but didn’t work in people, programs that consumed time, capital, and talent, yet stopped short of helping patients.

Drug discovery begins with an overwhelming choice.

There are tens of thousands of possible biological targets and millions of potential molecules. Only a vanishingly small fraction will ever become medicines. The earliest decisions - what to pursue and what to ignore - shape everything that follows.

When those decisions are wrong, the cost is paid years later: failed clinical trials, delayed treatments, and patients still waiting. Despite major advances across the life sciences, early-stage drug discovery remains disproportionately inefficient. In practice, it is still more about searching than solving. For patients, this inefficiency is not theoretical. It manifests as time lost, uncertainty prolonged, and treatments delayed.

Why Early Decisions Fail So Often

Scientists are forced to make high-stakes choices with incomplete information. Several structural challenges make this unavoidable:

• Biology is deeply complex. Many diseases are still partially understood, and choosing the right target often relies on educated guesswork.
• Early models are imperfect - what works in a lab dish or on an animal frequently fails in humans.
• The chemical space is vast - traditional discovery relies on exploring what is easy to synthesize and test, not what is most likely to succeed.
• Safety and pharmacology are hard to predict early - many candidates fail late due to toxicity or poor drug-like properties that were not obvious at the start.

These challenges compound over time. Early missteps quietly propagate until they surface years later - after enormous cost has already been incurred.

The biggest opportunity in drug discovery, therefore, is not to test more molecules.

It is to make better decisions earlier.

Peptris’ Approach: Playing the Game Differently

In the popular game of Tetris, you don’t lose because of one bad move - you lose because of a few early pieces that were “almost right.” They don’t fail immediately, but they create gaps, reduce flexibility, and leave you with fewer good options as the game progresses.

The best players win by recognizing early which pieces fit the board and which ones should be skipped.

This is exactly the problem Peptris is built to solve in drug discovery.

Peptris is an AI-powered preclinical drug discovery company designed to improve decision-making at the earliest stages of discovery. Instead of relying on large-scale trial-and-error in the lab, Peptris uses AI to understand chemical space, predict outcomes, and design better molecules before wet-lab testing begins.

In simple terms, Peptris focuses on deciding which molecular “pieces” are worth placing - and which should be discarded - early enough to preserve flexibility, reduce downstream failure, and dramatically improve the odds of success. This leads to a discovery in <50% of the time and <2% of the cost.

How does Peptris do this?

• Learning from large molecular datasets: Peptris models chemistry as a structured representation problem, enabling its systems to learn patterns associated with efficacy, safety, and drug-like behavior across large molecular libraries.
• Generating and prioritizing candidates: The platform does not limit itself to known compounds. It can propose novel, synthetically feasible molecules optimized for specific targets and constraints.
• Scoring against real-world constraints: Candidate molecules are evaluated across multiple dimensions simultaneously, including binding potential, safety, stability, and manufacturability - factors that often derail programs later if not addressed early.
• Validating through external laboratory partners: Peptris operates a “virtual R&D” model, maintaining internal focus on computational discovery while leveraging CRO partners for synthesis and experimental validation. This enables speed and capital efficiency without heavy infrastructure investment.

What stood out to us was not just the technology, but how clearly Peptris is focused on outcomes that matter in pharma. Value is created through:

• Discovering novel chemical entities
• Repurposing approved or clinically safe drugs
• Rescuing shelved clinical candidates by identifying new targets or indications

Why Now: A Converging Inflection Point

• Industry demand for more efficient discovery is intensifying. Pharma R&D budgets remain large, yet late-stage failure rates persist, increasing openness to approaches that improve early-stage filtering and design quality.
• AI methods relevant to molecular representation and generation have matured substantially, making in silico–first discovery meaningfully more viable than in prior cycles.
• Regulatory and societal pressures are also rising - to reduce unnecessary animal testing, improve predictability, and justify development costs. Stronger early-stage screening is increasingly becoming a baseline expectation.

Importantly, Peptris itself has moved beyond the conceptual stage. The company has:

• 13 molecules discovered across Oncology, Alopecia, Inflammation, and Rare Disease of which 5 have been patented
• Very 1st Indian startup to secure drug licensing for human clinical trials on a rare disease in just 3 years.
• Peptris’ AI-discovered small molecule focused on muscular dystrophies has received Orphan Drug Designation from the US FDA in Jan 2026 - the first of its kind from India

The current phase is one of scale: expanding the number of programs, accelerating iteration cycles, and deepening validation. This investment is designed to support that transition.

Our Conviction: Building a New Kind of Drug Discovery Company from India

For BYT Capital, investing in Peptris is about backing a team that is changing how early drug discovery decisions are made - making the process faster, more cost-effective, and more reliable

With Peptris, more drug programs will reach patients. Rare disease efforts will face fewer economic barriers. And the global pharma ecosystem will gain access to licensable assets developed with a fundamentally better cost-and-speed profile.

Drug discovery will always be complicated. But by playing the game more intelligently - by choosing the right pieces earlier - the odds can drastically improve in our favour.

That is why we invested.