Biotech & Pharma
Custom AI built for the science of new medicine.
General-purpose AI assistants were trained on the internet. Your drug program wasn't discovered on the internet. Your molecules, your assay data, your structure-activity relationships, your program history — none of it is in a foundation model's training set. And none of it should be in someone else's training pipeline.
Syntic partners with biotechs and pharma companies to build dedicated AI models around your targets, your molecules, and your proprietary data — deployed inside your environment and owned by you. Not a subscription to a platform that also serves your competitors. A model built for your program, your data, your milestones, and your IP boundary.
How we work with you
A program, not a subscription.
Every biotech and pharma engagement at Syntic is scoped to your science, your data, and your milestones. We don't sell platform seats. We don't offer a generic AI tool and ask your scientists to figure out the prompts. We partner on a multi-year program that delivers a model trained on your data, the workflows your scientists actually use, and the validation evidence your QA and regulatory teams require.
Step 1
Scope
A 4-week fixed-price scoping engagement before any commitment to a full program. We map your data — what you have, what's missing, what's clean, what needs work. We map your targets, your modalities, your program history, and your compliance requirements. We sign your NDA before the first conversation. The output is a written program plan with clear milestones, deliverables, pricing, and a delivery timeline. No commitment beyond scoping until you've reviewed the plan and decided to proceed.
Step 2
Build
We assemble a dedicated team of ML researchers, computational chemists, a regulatory science specialist, and a compliance lead — matched to your modality and your program stage. The team builds inside your environment: your VPC, your on-prem infrastructure, or your sovereign cloud. Your data never leaves your perimeter. Your IP never touches a shared training environment. We work alongside your scientists, your QA team, and your IT security organisation from day one.
Step 3
Deliver
The trained model, the validation evidence package, and the integration with your scientists' existing workflows. Frozen and versioned for GxP-aligned use. 21 CFR Part 11 supporting controls documented and validated per engagement. The model is yours to operate — not dependent on Syntic's infrastructure staying live, not subject to Syntic's pricing decisions, not shared with any other customer. Yours.
Step 4
Partner
A multi-year ongoing partnership. As your program generates new data — new assay results, new clinical readouts, new regulatory guidance — the model retrains on it. New programs within your pipeline can extend the engagement. Regulatory guidance changes across FDA, EMA, PMDA, and other authorities are tracked and routed to the right owner before they affect your filings. We don't deliver a model and disappear. The partnership lasts as long as the program does.
Where Syntic helps
From target identification to regulatory filing — the same partner, at every stage.
The model and the workflows tune to where your program is. Discovery looks different from development. Development looks different from filing. The partnership scales with the science.
Discover
From target to candidate
Custom-built models for the discovery pipeline, trained on your structures, assays, and program history.
- Target identification. Triangulate published literature, omics data, internal assay readouts, and competitive intelligence into evidence-graded target landscapes. Every claim linked to its source. Every gap in the evidence flagged explicitly. The literature synthesis that takes a biology team months, compressed into days — without losing the scientific rigour the decisions downstream depend on.
- Molecular generation. Generate and prioritise candidate structures against your design constraints — your target binding requirements, your ADMET profile goals, your synthetic accessibility limits, your IP landscape. Models trained on your historical program data, not on public structures alone. The candidates that come out reflect your chemistry, your SAR history, and your programme's specific design criteria.
- ADMET and safety prediction. Early flags on absorption, distribution, metabolism, excretion, and toxicity — trained on your historical assay data alongside published datasets. Off-target risk surfaced before expensive in vivo studies. The predictions that send a molecule into the next round versus the ones that pull it — made earlier, with the reasoning attached for your scientists to interrogate.
Research
From a library of papers to a plan
Compress the reading, cross-referencing, and write-up of a research team from months into days, with every claim traceable to its source.
- Literature synthesis. Tens of thousands of papers, patents, conference abstracts, and clinical trial readouts — triangulated into a ranked, fully cited competitive and scientific landscape. Every claim traceable to its source. Contradictions in the literature flagged, not smoothed over. The background section of your IND or your CSR that used to consume a research team for six weeks, compressed into days — with the citations your medical writers and regulatory reviewers will check.
- Hypothesis and experiment design. Propose mechanisms consistent with your data, design experiments to test them, flag the controls a scientific reviewer or an FDA reviewer would ask for, and surface the alternative hypotheses your team should rule out. The intellectual scaffolding that makes a good scientist great — available to the whole team, not just the PI with thirty years of pattern recognition.
- Lab and assay data. Analyze assay results, surface structure-activity relationships, and write up the findings with linked data.
Develop and file
From hypothesis to filing
The regulatory and clinical paperwork that gates a program, first-drafted with citations and a full audit trail.
- Regulatory drafting. INDs, NDAs, BLAs, MAAs, CSRs, briefing documents, and responses to agency questions — first-drafted with citations, tracked rationale for every claim, and explicit flags for the scientific and regulatory judgements your team needs to make. The IND that takes a regulatory affairs team four months gets a structured first draft in days. The briefing book for a Type B meeting gets drafted alongside the meeting request. Your regulatory team reviews, refines, and signs — they stop writing from blank and start editing from substance.
- Trial operations. Protocol deviations, monitoring summaries, and site communications with a complete audit trail.
- Regulatory intelligence. Track guidance changes, draft guidance documents, advisory committee outcomes, and enforcement trends across FDA, EMA, PMDA, Health Canada, TGA, and equivalent authorities in every jurisdiction your program is filed in. Route relevant changes to the regulatory affairs owner who needs to act, with a plain-English summary of what changed, what it means for your program, and what needs to happen before your next milestone. The guidance change that slips through and surprises you at a pre-NDA meeting becomes the guidance change you planned for twelve months in advance.
Your model, trained on your molecules
This is the core of what we do. The edge in drug discovery isn't the algorithm — it's the data. Your proprietary structures, your assay history, your failed programs and the patterns in why they failed, your SAR knowledge that took a decade to build. That data is your competitive moat. It shouldn't train a model that serves your competitors. It shouldn't leave your environment. And a model that wasn't trained on it won't be as good as one that was.
Syntic builds dedicated models trained on your proprietary data, owned and operated inside your environment. One customer. One program. One model that reflects your chemistry and your history. Each engagement is custom, scoped to your modality, your data volume, your program stage, and your regulatory milestones. Not a subscription. Not a generic platform with your logo on it. A model built for your program and no one else's.
- Trained on your proprietary structures, assays, and program history
- Modality-specific: small molecule, biologics, peptides, RNA
- Deployed on-prem or in your VPC, your IP never leaves your environment
- Frozen and versioned for validated, GxP-aligned use
- Built and tuned alongside your scientists as a long-term partnership
Compliance & IP
Scoped to your environment, your regulations, and your IP boundary from day one.
Drug development happens in one of the most regulated environments in the world. The compliance requirements aren't an afterthought we bolt on at the end — they're the starting point for every engagement. We work with your QA, IT security, legal, and regulatory affairs teams from the first scoping conversation to make sure the controls match your environment, your programme stage, and the authorities you file with.
Customer data and IP never train models outside your engagement
Audit logging and review trails designed for GxP-aligned use
On-prem and VPC deployment available, your IP stays in your environment
Region-pinned data residency available (US, EU, JP)
21 CFR Part 11 supporting controls, scoped and validated per engagement
Validation evidence package delivered as part of each program
BAA and DPA available
EMA Annex 11 considerations for computerised systems in GxP environments
PMDA guidance alignment for Japanese regulatory submissions
ICH E6(R2) and E6(R3) GCP alignment for clinical trial data workflows
Custom compliance scoping per programme, per jurisdiction, per regulatory pathway
Compliance scope is set during the 4-week scoping engagement, based on your specific program requirements.
FAQ
Frequently asked questions
The questions biotech and pharma teams ask before scoping a custom program. If yours isn't here, our life sciences team will answer it directly.
About the engagement
How long does a typical engagement take?
The scoping engagement is 4 weeks, fixed-price. A full program — from scoping through model delivery and workflow integration — typically runs 6 to 18 months depending on data readiness, modality complexity, and the number of workflows in scope. Multi-year partnership arrangements are standard for programs that continue to generate new data and new milestones. We'll give you a specific timeline in the written program plan delivered at the end of scoping.
What does it cost?
Engagements are priced per program based on scope, modality, and compliance requirements. Typical custom-model programs range from low six figures for a focused proof-of-concept to multi-million-dollar, multi-year partnerships. The 4-week scoping engagement is fixed-price and delivers a written program plan with pricing before any commitment.
Is this a subscription or a project?
A project. We don't sell seats or a SaaS subscription for custom programs. Each engagement is scoped, priced, and delivered as a partnership, multi-year for most customers, with ongoing retraining and support as your data grows.
Can we see a reference customer?
We are in active engagements with biotech and pharma partners under NDA. We can arrange a reference call with an existing customer once your program reaches the scoping stage and an NDA is in place.
About the model
What model is the custom model based on?
That depends on the modality, the data, and the deployment constraints. For some programs we build on open-source foundation models such as chemistry-specific transformers. For others we train from scratch on your data. The base architecture is one of the decisions made during scoping, alongside your compliance and IP team.
Who owns the trained model?
You do. The trained model, the weights, the validation documentation, and the integration code are yours. They're deployed in your environment, operated by your team, and not dependent on Syntic's infrastructure. If the partnership ends, you keep the model. We don't hold the weights as leverage.
Will our data be used to train models for other customers?
No. Your proprietary data trains models that belong to you and are deployed in your environment. It is never used to train shared models, never used to improve Syntic's general-purpose models, and never shared with any other customer. This is contractual, not just policy. Your NDA, your data processing agreement, and your master services agreement all reflect it explicitly.
How is the model deployed?
On-prem in your data center, in your VPC, or in a region-pinned managed environment. The deployment architecture is scoped during the engagement based on your IT and compliance requirements. Internet egress, key management, and update workflows are all defined per customer.
About compliance and validation
Are you 21 CFR Part 11 compliant?
Syntic provides supporting controls for 21 CFR Part 11 compliance — audit trails, access controls, electronic signature workflows, and system validation documentation — scoped and validated per engagement. We don't claim blanket Part 11 compliance as a platform certification because Part 11 compliance is determined by the validated system in your specific environment, not by the vendor alone. We work with your QA team and your validation specialists to ensure the controls in your deployment meet Part 11 requirements for your specific use case.
Do you support GxP environments?
Yes, with scoping. GxP-aligned audit trails and validation evidence are part of every engagement that requires them. We work with your QA team from kickoff to ensure the controls match your SOPs.
Can you sign a BAA?
Yes. Business Associate Agreements are available for engagements involving PHI. Data Processing Agreements are available for engagements involving EU data subjects.
What about data residency?
Region-pinned deployment is available in the US, EU, and Japan. Other regions are scoped per engagement.
About data and IP
What data do we need to provide?
It depends on the program. Typical inputs include proprietary structures such as SMILES, sequences, or 3D, plus assay results, ADMET data, and any historical program records you can share. The scoping engagement maps your available data against the program goals.
What if our data is messy or incomplete?
That's normal. Most biotech data is. Data preparation is part of the engagement. We work with your scientific team to clean, standardize, and structure the inputs before training begins.
Do you sign NDAs before the scoping call?
Yes, always. We sign your NDA before the first substantive conversation about your program, your targets, or your data. No exceptions.
About the team and process
Who works on our engagement?
A dedicated team scoped to the program: an ML research lead, computational chemists or biologists matched to your modality, a compliance lead, and a delivery manager. The team is named in the engagement contract.
How involved does our team need to be?
Most engagements work best with 1 to 2 dedicated scientific points of contact on your side, plus periodic touchpoints with your QA and IT teams. The scoping engagement defines the exact involvement.
What happens after delivery?
A multi-year partnership. We retrain the model as your data grows, support new programs, and track the guidance changes that affect your filings. Support and retraining terms are defined in the engagement contract.
Still have questions? Talk to our life sciences team.
Who we work with
Scoped to the science and the data — not the company size.
Biotechs running their first IND who need to move faster than their runway allows. Top-20 pharma extending an internal AI roadmap to programs where proprietary data makes a dedicated model worth building. CROs delivering computational and regulatory work for sponsors who need the IP boundary maintained. Academic spinouts with novel modalities and data nobody else has.
The common thread isn't size or stage. It's that the program has proprietary data, a milestone where speed matters, and an IP boundary that a general-purpose AI subscription can't respect.
If you have a target, a modality, or a milestone where a model trained on your data would change the timeline — that's the conversation to start.