Ingredient-to-function data.
Our proprietary data product captures identified connections across 400,000 ingredients and 17,000 biological endpoints. We map these connections to market applications.
Biology mapped to market opportunity.
OUR TECHNOLOGY / PROPRIETARY AI & DATA
A virtual cell.
A wider view of
ingredient potential.
Understand biological function. Explore ingredient combinations. Prioritise the evidence that could turn an idea into a differentiated product.
Explore the technology
SIFTLINK VIRTUAL CELL
Biology, computed.
Molecular relationshipsBiological responseTemporal perspective
BUILT AT SIFTLINK
Our proprietary data
and model stack.
Connecting biological function with market applications and clinical development, through data and models developed in-house.
Ingredient-function AI.
Our proprietary AI and deep learning model predicts ingredient function and helps prioritise new applications. Used in multiple projects, its predictions have been experimentally validated multiple times.
Predictions tested through experiments.
Clinical trial prediction.
Our in-house model assesses the likelihood of clinical success to help optimise future trials and identify emerging signals in ongoing studies.
Insight before and during clinical trials.
FOUR PERSPECTIVES ON INGREDIENT POTENTIAL
One ingredient is only
the starting point.
Composition, biological context and time shape the questions that matter for a differentiated product.
Find the biology behind the opportunity.
Our proprietary AI virtual cell model connects ingredient chemistry with biological networks and scientific evidence. It helps identify functions, endpoints and applications worth investigating.
Which applications deserve a closer look?
01 / MODEL PERSPECTIVE
Ingredient chemistry
Targets & pathwaysSIFTLINK VIRTUAL CELL
Application hypotheses
Predictions guide research and experimental validation. Composition hypotheses and population-level insights are not established efficacy or individual treatment recommendations.
OUR PROPRIETARY DATA PRODUCT
Ingredients. Products.
Functions.
Connected by evidence.
Our proprietary data product brings identified ingredient–endpoint connections, ingredient chemistry and product information into a structured foundation for discovery and market application mapping.
Curated source information is enriched with Siftlink’s own relationships, annotations and modelling. This proprietary layer connects a commercial question to the biology and evidence behind it.
How we turn evidence into decisionsTHE INFORMATION WE CONNECT
01
Ingredients & products
Identity · chemistry · compositions · product context
02
Biology & function
Targets · pathways · endpoints · biomarkers
03
Evidence & experience
Scientific findings · clinical outcomes · real-world signals
PROPRIETARY DATA + MODELSA connected basis for your next hypothesis.
IN-HOUSE CLINICAL TRIAL PREDICTION
Before and during a trial.
Better-informed decisions.
Our in-house clinical trial prediction model connects bioactives, biological mechanisms and clinical outcomes to assess the likelihood of a trial meeting its primary endpoint. It helps optimise future trial designs and identify emerging signals in ongoing studies.
RETROSPECTIVE TEMPORAL HOLDOUT0.893ROC-AUC · trial-outcome model
95% confidence interval: 0.838–0.939
Tested against later trial outcomes.
The reported evaluation trained on evidence before 2020 and tested on later trials. ROC-AUC measures how well the model ranks successful outcomes above unsuccessful ones.
0.734
Discuss the methodology ROC-AUC for the separate structure-and-graph-only evaluation, without trial information.
Siftlink-reported retrospective benchmarks. These results do not establish prospective trial success or uniform performance across endpoints. Methodology and programme-specific applicability are available for discussion.
SIFTLINK RESEARCH / 2026216,350community comments analysed
REPORTED EXPERIENCECLINICAL RECORD
Can real-world experience help us identify what deserves a closer look?
OUR REAL-WORLD DATA STUDY
Listen to experience.
Test it against
the evidence.
Our study examined whether ingredient benefits reported in online communities align with the clinical literature. It connected community reports and clinical findings through a shared ingredient–benefit framework.
Repeated community reports were more likely to have clinical support in the study. This offers a route to prioritising questions for research; community reports alone do not establish efficacy.
2026 PREPRINT · NOT PEER REVIEWEDRead the RWE studyGkountakos, Goulas & Kourmpetis. “Can social media forums serve as real-world data for nutraceuticals?” medRxiv, June 2026.
TECHNOLOGY WITH A DEVELOPMENT PURPOSE
Put the model to work
on your next question.
INNOVATION SPRINT
Find a new application.
Explore your ingredient’s biological potential and prioritise commercially relevant opportunities.
Explore the sprint PRODUCTSBuild the next evidence.
Turn a candidate, composition or process hypothesis into a development programme.
Explore a partnership SCIENTIFIC & INDUSTRIAL EXPERTISEMeet the people behind it.
Science, AI, intellectual property and innovation management, working on the same question.
Meet our teamSTART WITH THE SCIENTIFIC QUESTION
What would you like
to understand next?
Bring an ingredient, composition or clinical development question.