Canalytics gives cannabis research, on day one, the consolidated analytical foundation that other therapeutic categories took decades to build. Twenty-plus years of research are synthesized into a suite of analytical tools for understanding cannabis through its chemistry in every context, with 10,213+ peer-reviewed studies powering a standardized effect framework and receptor-level mapping for every cannabinoid and terpene. A ten-pathway chemovar alignment framework covers 15 cancer types, and a clinical recommendation engine operates at the depth pharmaceutical research requires.
Mechanism-to-outcome mappings become testable, cohort selection becomes empirical and curriculum rests on a standardized, research-backed reference.
A 100,000+ phenotype universe without a structured search framework. Receptor pharmacology assembled study by study for every course and protocol. Translational studies that require a validated decision support layer to test clinical implications against. Programs seeking pharmaceutical partnerships that require the analytical depth sponsors expect.
A large number of cannabinoids and terpenes mapped to their receptor affinity and activity across CB1, CB2, TRPV1, TRPA1, TRPM8, 5-HT1A, D2, GPR55, PPARγ and opioid μ. A standardized 30-effect framework across the literature. For cancer research and chemovar alignment: chemovars evaluated across ten weighted pathways to identify which cannabinoid and terpene profiles clinical research most consistently associates with each cancer type. Canalytics Breeding Software simulation for breeding studies. A HIPAA-grade, FDA 21 CFR Part 11 aligned recommendation engine for regulated translational research.
Every output is deterministic and supported by a robust study database, which is exactly what a protocol, a thesis committee and a pharmaceutical sponsor each require.
Pharmacology reference, cancer chemovar alignment analytics, breeding research, cohort selection and translational decision support
Advanced machine learning analysis leveraging detailed mappings of which receptors each cannabinoid and terpene modulate — with evidence strength and mechanism of action — gives researchers a deeper understanding of compound-level impact, supports mechanistic hypothesis development, and helps determine which cannabis genetics are best aligned to the literature's documented understanding of cannabinoid and chemovar effects for each study.
Analyze cannabis genetics across 15 cancer types, each evaluated across ten weighted pathways of compound efficacy in the literature: tumor growth inhibition, apoptosis induction, cell cycle arrest, anti-metastatic activity, anti-angiogenic activity, symptom management, oxidative stress modulation, immune modulation, autophagy and synergy with conventional care. Each score identifies, for a given cancer category, the cannabinoid and terpene concentrations supported by the clinical studies with the most consistently significant results. Translational researchers build protocols against pathway-specific chemovar alignment outputs.
Analyze thousands of cannabis chemovars at once and cluster the ones that score highest for each cancer type and treatment pathway, surfacing the most impactful candidate phenotypes for further study. Discover the natural groupings hidden in a scored library, identify which compounds actually drive an outcome, and flag the chemovars that behave unlike anything else in the set, so hypotheses start from the structure of the data rather than from strain names.
Pre-select the top fraction of phenotypes from a large candidate population for clinical or agronomic research by any effect, multi-effect profile or receptor-mediated target. Every candidate receives a 0 to 100% match against the defined research specifications, making cohort selection empirical and reproducible.
Simulate millions of breeding expressions across genetic libraries to find the strongest parent strategies for a targeted effect or medical outcome, and breed intelligently to strengthen the therapeutic profile of pre-existing medically effective genetics. Once the offspring are grown, empirical effect analysis of the phenohunt adds a second layer of proof: every phenotype's COA is scored against the predicted profile, verifying and validating the simulation and feeding the results back into the next round of crosses.
The Cannabis Clinical Suite is the robust scientific decision support layer translational research has lacked. NIH-validated assessment as trial intake, per-condition product scoring, drug interaction analysis and HIPAA-grade architecture aligned to FDA 21 CFR Part 11 and ICH E6 GCP make it deployable in regulated studies.
Cancer Chemovar Alignment provides the cancer-specific chemovar scoring framework. Business and Plant Match provide the pharmacology and chemovar infrastructure. The Cannabis Clinical Suite supports translational clinical studies. Breeding supports agricultural and genetics programs.
Regulated-study-ready clinical recommendation with full traceability.
Ten weighted pathways, 15+ cancer types, synergy modeling and the full ML analytics suite for research-grade chemovar alignment scoring.
Search the genetic universe for chemovars matching any cannabinoid and terpene target profile — including hypothetical and designer profiles reverse-engineered from research.
Canalytics Breeding Software simulation and multi-effect optimization for breeding studies.
Receptor mapping, multi-tab comparison and clinical NIH-validated assessment workflows.
Multi-cohort and multi-location comparison across scored populations.
A deployment-ready analytical foundation for research, curriculum and pharmaceutical partnership
The multi-year effort of assembling pharmacology references, structuring the chemovar landscape and building decision support from primary literature is already done. Programs move directly to hypothesis, protocol and cohort, with the reference layer in place and backed by a robust study database.
Canalytics analyzes cannabis genetics and effects with proprietary algorithmic and machine learning analysis, not an AI black box. Every effect score, similarity match, pathway score and recommendation follows an explicit calculation from input chemistry to output, with the compound, mechanism and source behind it. An AI model cannot fully show or reproduce the sequence of neural network processes that underlie its responses. That is not the case with Canalytics: every result is trackable to its inputs and identically reproducible on every run. Results can be inspected, reproduced and defended, which is the standard research requires.
Pharmaceutical entry into cannabis makes universities with cannabis programs natural partners, and sponsors look for receptor-level mechanism, multi-pathway analysis and IND-supporting documentation depth. The Pharma-Grade Designer Cannabis Formulas tier gives a program that infrastructure and a direct partnership with the Canalytics scientific team.
Graduate cannabis pharmacology courses, integrative oncology and cancer chemovar alignment fellowships and applied genetics programs teach from a standardized effect vocabulary, receptor map and simulation engine instead of rebuilding content that exists immediately in other therapeutic categories.
From research question to structured, scored, reproducible analysis
Upload or photograph COAs for the chemovars, phenotypes or products in scope, or work from the pre-loaded database with full receptor and pathway documentation. Define custom hypothetical target profiles where needed.
Every entry is scored across all effects, mapped to receptors, evaluated across the cancer chemovar alignment pathways and available for clustering, dimensionality reduction and feature importance analysis.
Rank candidates against the target, select cohorts empirically, and build protocols against pathway-specific outputs and mechanism mappings with full traceability documentation.
Deploy the clinical recommendation layer in translational studies, validate breeding predictions in the field, and report results with the research context behind every figure, backed by our proprietary study database.
Everything a protocol, a committee or a sponsor asks for
Every product Canalytics analyzes is scored across 30 therapeutic and adult use effects on a 0 to 10 scale, from the full cannabinoid and terpene fingerprint in its COA. This is the shared vocabulary that lets a breeder target an effect, a dispensary sort a shelf by it, a marketer write to it, and a clinician recommend against it.
Condition-relevant effects scored from receptor-level pharmacology, so clinical positioning and evidence-aligned recommendations rest on science.
Experience-relevant effects that predict how a product actually feels, so customers can shop by outcome instead of by strain name and THC percentage.
Beyond the effects shown above, Canalytics scores 17 additional therapeutic and adult use effects on every product, delivering the most complete effect profile in the industry and the depth needed to filter for what a customer wants and against what they want to avoid.
Every effect is quantified, not described. Products become sortable, comparable and rankable against any target.
Find high Calm and low Couchlock, or Focus without Anxiety. Score for the effects wanted and against the effects to avoid.
Cannabinoids and terpenes mapped to CB1, CB2, TRPV1, 5-HT1A, PPARγ, opioid μ and more, so scores rest on mechanism.
Every score is supported by a robust study database of peer-reviewed research. No black box.
Use Canalytics as the analytical infrastructure for cannabis pharmacology, cancer chemovar alignment research, genetics and translational studies.
Run Business and Plant Match against an in-development research project. Add Cancer Chemovar Alignment for cancer-focused research programs and the Cannabis Clinical Suite for translational clinical studies. Curriculum integration follows immediately from the same reference layer.