From authorized knowledge to evidence-backed discovery decisions
NEWMA is a proposed platform that connects authorized ethnobotanical knowledge and authenticated botanical materials to computational prioritization, controlled experiments and scientist-approved observations, while keeping source attribution, confidentiality and benefit obligations attached.
The demo uses synthetic data and is not evidence of scientific performance, deployment or compliance.
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What NEWMA is
NEWMA is proposed as a hybrid computational and experimental platform for ethnobotanical drug discovery.
The problem it addresses
Discovery decisions stall because knowledge, physical material, chemical identity and biological evidence are hard to connect reliably.
How it works
Agentic discovery
A scientist submits a query, an objective and constraints. The agent is designed to check access and rights first, retrieve only within the authorized scope, and return ranked hypotheses with their limitations.
Durable screening
Screening requests are designed to run as durable workflows with budgets, bounded retries, cancellation and holds, recording inputs, versions, settings and seeds.
Wet-lab loop
Scientists approve assay requests. Results return with raw data, replicates and uncertainty, and only observations a scientist accepts count as evidence.
Signed provenance
Decisions and records are designed to carry signed, versioned provenance. A ledger layer is an optional extension, and authoritative records stay off-chain.
Computational outputs remain hypotheses. Scientists approve experimental work and advancement.
See the demoWho it is for
Computational biologist
Needs reliable chemical identities, reproducible runs and a clear reason a candidate merits testing.
Wet-lab scientist or CRO
Needs unambiguous materials, protocols and controls, so accepted observations link to the right batch and hypothesis.
Indigenous community liaison
Needs understandable consent, control over disclosure and visible benefit obligations, without exposing confidential knowledge.
Biopharma partner
Needs secure discovery access and traceable evidence to judge scientific and commercial readiness.
Supporting roles include tenant administrators, scientific approvers, data stewards, legal reviewers and security operators.
The six components
Each component has its own page, with the sources it is drawn from.
About LivFul
Mission
LivFul exists to enable research teams to turn authorized knowledge and authenticated materials into reproducible, experimentally supported decisions, preserving attribution, confidentiality and benefit obligations.
Vision
A rights-aware evidence system, scientist-supervised computation and an assay feedback loop, built on shared infrastructure.
Approach
- A rights-aware evidence system connects botanical knowledge to authenticated materials and curated structures.
- Scientist-supervised computation prioritizes the experiments worth running.
- An assay feedback loop records confirmed activity, failures and development liabilities.
These are design goals for a proposed platform, not results achieved.