Developability screening for Indian biologics teams, confirmed in a lab
TL;DR Stablecraft screens antibody candidates before a company commits to cell-line work. It flags aggregation, viscosity and immunogenicity risk computationally, then confirms the top flags in a partner lab. Indian biologics and biosimilar developers pay per programme, and what they get is a ranked shortlist with evidence.
Structure prediction stopped being the bottleneck a while ago. The leading public structure database holds more than 200 million predicted structures under a CC-BY-4.0 licence, free for academic and commercial use[1], and its latest model reports higher accuracy for protein-ligand and antibody-antigen prediction than earlier methods[2]. Anyone can fold a protein now.
What still goes wrong happens later, and it costs a lot more. A 2026 review in Antibody Therapeutics says it plainly - strong binding alone does not guarantee that a molecule can be manufactured, stays stable or behaves safely[3].
India has a big stake in getting this right, with biopharma at US$58.4 billion inside a bio-economy of US$165.7 billion in 2024[4]. A candidate that aggregates at high concentration gets found out after months of process work.
Stablecraft takes a sequence set, usually 20–200 variants from a discovery or biosimilar programme, and scores each one for the properties that kill molecules downstream. That means aggregation propensity, viscosity at high concentration, chemical liabilities in the binding loops and predicted immunogenic sequences. Every flag comes with the model's reasoning and its uncertainty, because the same review warns these models should prioritise experiments rather than replace them[3].
The second half is wet. The top and bottom candidates go to a partner lab for a small set of confirming assays, and each report puts the measured result next to the prediction. Over time that builds a private calibration set, which is the real asset.
The seed wanted a protein structure prediction platform. I reframed it because free tools already do that job well[1][2]. Selling a decision backed by measurement is a business a small Indian team can actually defend.
The buyer is the head of biologics discovery or the CSO at an Indian biosimilar or novel-biologics company, plus CROs running discovery for others. They already spend on developability testing, just later and in the lab. The sale is a per-programme fee with a wet-confirmation pass. That pricing is my assumption, because I found no Indian list prices for this service. Academic groups are a poor payer here and deserve a cheap tier at most.
India's BioE3 policy of 2024 named precision biotherapeutics a thematic priority and funds shared bio-enabler facilities[4]. Indian biosimilars are being studied in real-world use, including biosimilar trastuzumab emtansine in 116 patients across adjuvant and metastatic settings[5], which shows how many programmes are now in play. On the method side, the latest structure-prediction model arrived in 2024[2] and the 2026 review sets out how far machine learning can be trusted on developability[3].
There's no public figure for what Indian companies spend on computational developability, and I found none for the number of approved similar biologics either. The narrow signal is programme count. Each biosimilar or novel antibody programme is a possible ₹20–60 lakh engagement at my assumed pricing, so a few dozen active programmes a year is the whole addressable base in India at the start. Biopharma was US$58.4 billion of India's bio-economy in 2024[4], but that's adjacent context only.
One Indian AI firm works with biopharma on antibody, peptide and enzyme engineering with its own platform[6]. Another sells target validation on multi-modal data and names large pharma partners[7]. A US design platform sits closer, with expression vector design and model-guided sequence work for antibody formats, though it publishes no prices[8].
All three aim at well-funded programmes. The gap is a mid-priced, measurement-backed screen for Indian developers who can't buy a platform licence.
| Difficulty | Hard (54/100) |
| Starting capital | ₹3–6 Cr |
| Time to launch | 12–18 months |
The science is the hard part. Public developability data is thin, so your predictions will be weakly calibrated until you've run your own assays. Hiring is tough too, because people who know both antibody engineering and machine learning are scarce and expensive. And customers will test you against their internal results before they pay a rupee.
No product licence. Computational screening sold as a research service to companies isn't a regulated product in India, but a few rules still apply.
Say in every report that nothing in it supports a clinical or regulatory claim.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
AI ranks candidates, but it doesn't replace the assay. Never let a prediction stand in for measurement when a client is deciding which molecule to take forward.
Want to work out the AI layer for Stablecraft? Talk to Yash →
This needs a founding pair - a protein scientist with developability experience and an ML engineer, ideally with a CRO relationship already in hand. If you're a pure software team then skip it, because the wet confirmation is the spine of the product. Before raising money, run a blind test on 20 molecules a client has already measured and show your ranking matches theirs on the worst and best candidates.
Plan for roughly ₹3–6 Cr, most of it on the founding scientists, compute and paid partner-lab assays.
Not a product licence, because it's a research service. Wet confirmation has to happen in a registered lab under biosafety rules, and shipping constructs needs transfer agreements.
Yes, biologics developers buy this worldwide. You'd face more competition abroad, so you'd need a partner lab there and a clear reason why your calibration data is better.
The free Stablecraft validation kit has the line-by-line capital breakdown, the effort scoring, the rules and contract points that apply, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for biologics leads, and 5 assumptions to test. Download the kit →
Thinking about building Stablecraft or something close to it? The make-or-break bit is a blind test against a client's own measurements and a lab partner who turns confirmations round fast, and I can help you set up both. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.