Zyntar
Capabilities

Methods that turn data into decisions

Nine deployable capabilities across pharmaceutical R&D — each shown through a real case study, with the approach, the numbers, and the outcome.

Case study · Quality by Design

Finding the optimal solution

01

Know more by doing less — a full QbD response-surface approach that pinpoints the optimum and the interactions between factors.

The challenge

Three variables — pH, Variable 1 and Variable 2 — were critical to system stability, but the optimal combination of values was unknown.

Our approach

A CCC design of just 16 experiments, followed by modeling, defined the ranges and pinpointed the optimum — together with the interactions between factors.

16experiments to optimum
pH 3.2Var 1 · 0.9 g / Var 2 · 72%
  • Lead solution discovered
  • Factor interactions quantified
  • Patented results, full QbD approach

When it's practicalBest when optimizing multiple variables with as few runs as possible.

Response surface for variable optimization
Response surface for variable optimization
Case study · Factor Importance

What drives the outcome — when everything moves at once

02

Importance ranking from regression & ML, capturing the multimodal interactions that one-at-a-time analysis misses.

The challenge

When many factors vary at the same time, one-at-a-time analysis breaks down — the real drivers hide in interactions between variables, not single effects.

Our approach

We fit regression and machine-learning models across all factors at once — regularized regression, random forests, gradient-boosted trees — then extract feature-importance and interaction terms to rank what truly moves the response.

All factorsmodeled together
Multimodalinteractions surfaced
  • Dominant drivers identified & ranked
  • Hidden interaction effects surfaced
  • Focus narrowed to the factors that count

When it's practicalBest when many variables move together and the key effects live in their interactions — for example, evaluating the effects of several excipients at the same time.

Factor importance ranking from regression and ML models
Relative factor importance from regression & ML models
Case study · Advanced Analytics

Patterns in any system

03

From raw data to deployed models — maintainable, production-grade predictions teams can trust.

The challenge

Finding reliable patterns in complex data — and turning them into maintainable, production-grade predictions teams can trust.

Our approach

We build custom data pipelines and apply state-of-the-art algorithms — Random Forests, SVM, XGBoost, Naive Bayes, neural networks and gradient boosting — with hyperparameter optimization and at least 10-fold cross-validation, then deploy and maintain them with KPI dashboards (PowerBI, Tableau, custom).

All dataunified in one place
Stablevalidated, reliable predictions
  • Reliable production models
  • KPI dashboards for reporting
  • Patterns turned into decisions

When it's practicalBest when you need custom, deployable analytics tuned to your own data.

Stability analytics dashboard on a custom molecular database
Stability analytics on a custom molecular database
Case study · Similarity Search

Simpler and better

04

Find better molecules — rank safer, simpler alternatives against a reference compound.

The challenge

A company relied on complex cyclic oligosaccharides for a beneficial stabilizing effect — but the molecule was risky on safety and expensive. Was there a better option?

Our approach

We described the molecule with five fingerprints, then ran a similarity search against a custom FDA IID database using Tanimoto and related metrics to rank safer, simpler alternatives.

Newcandidate found
Simplermolecule, less safety risk
  • A simpler, lower-risk molecule
  • More effective stabilization
  • Insight into mechanism of action

When it's practicalBest when known actives exist and you want safer or cheaper analogs of a successful lead.

Complex stabiliser reduced to a simpler, lower-risk analog
Complex stabiliser → simpler, safer analog
Case study · Clustering

Mapping a 1000-compound space

05

Test representatives, not everything — a data-driven map of a large chemical space.

The challenge

A company needed to screen roughly 1000 compounds but could test only one per day — about 1000 days of work.

Our approach

We described and clustered the compounds, then tested the edge and center compounds of each cluster as representatives — a data-driven map of the space.

1000 → ~150tests required · ~7× faster
High confidenceno hits missed
  • Tests cut from 1000 to ~150
  • Feasibility ahead of schedule
  • Confidence no hit was missed

When it's practicalBest for mapping and prioritizing large, multivariate spaces.

Compounds grouped by similarity through clustering
Before / after — compounds grouped by similarity
Case study · Databases

Keep your secrets to yourself

06

Data storage that suits your needs — private, local and curated to your screening criteria.

The challenge

A company wanted to screen against compounds that are FDA approved, available to order, and with no described medical effects — but no such database existed.

Our approach

We scraped trusted sources (FDA, PubChem and others) and built and maintain a custom SQL database, curated to their needs and kept private and local.

Privatelocal & confidential
ML-readystructured & queryable
  • Confidential and local
  • More specific results
  • Permanent, maintained asset

When it's practicalBest when off-the-shelf data can't meet your screening criteria.

Custom molecular database composition by source
Database composition — FDA, DailyMed, PubChem, ChEBI & in-house
Case study · Drug Discovery

AI-powered drug discovery pipeline

07

Built for speed, precision and scalability — from real experimental data to synthesized candidates.

With 12 predictive models, 5 generative models, and 15 molecular fingerprints (including proprietary) supported by advanced optimization, we unite ligand-based, structure-based docking, and generative methods to rapidly explore chemical space, identify top candidates, and deliver a ranked candidate list — or already-synthesized molecules.

AI-powered drug discovery pipeline diagram
From real experimental data to final candidates — docking, ML & generative recomposition
Setup
  • Define ligand libraries (global + curated)
  • Ligand / receptor preparation
  • Select fingerprints & descriptors
  • Set objectives: affinity, selectivity, safety
Predictive & Generative
  • Ligand-based ML models (RF, SVM, ensemble fingerprints)
  • Structure-based docking (Vina, GNINA, SMINA, CNN score)
  • De novo generation, scaffold hopping & retrosynthesis
  • Molecular dynamics (MD) & ADMET AI filtering
Output
  • Ranked list of novel, not-yet-existing molecules
  • Synthetic accessibility & safety integrated
  • Validated across ML, docking & dynamics
  • 1–2 grams of top candidates synthesized
Track record · IP-Proven Discovery

Discovery that becomes defensible IP

08

We've done it — and it holds up. Discovery pipelines designed from the start to generate protectable IP.

What we deliver

We build AI/ML discovery pipelines designed from the start to generate protectable intellectual property — and we've delivered them on real programs, with results that hold up.

Multiplepatent filings
Provenon real programs
  • Patent filings across novel drugs, forms & methods
  • Pipelines built to preserve patentability
  • Methods validated on live R&D programs

Bottom lineWe can do this for your program — and we already have.

ML discovery turning into patent-ready, defensible IP
ML discovery → patent-ready, defensible IP
Track record · Funding & Leadership

We win and deliver EU-funded research

09

Proven from proposal to delivery — competitive EU funding, coordinated to completion.

What we deliver

We've written, won and led competitive EU-funded research applying data science to pharmaceuticals — coordinating partners all the way through to delivery.

Awardedcompetitive EU funding
Deliveredmulti-partner projects
  • Competitive EU grants secured
  • Multi-partner consortia coordinated
  • Research translated into execution

Bottom lineWe can secure and lead funding for your initiative — we've done it before.

Let's talk

Bring a capability to your program

Tell us the decision you're trying to make — we'll show you the method that gets you there.

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