AI across the R&D lifecycle.
Connecting discovery, validation and development.
We have built an AI framework spanning program evaluation, candidate screening, experiment design, process development and program management. Evidence integration sets research priorities, experimental feedback refines development paths, and dynamic resource planning supports execution.
Intelligence at every step.
Progress with purpose.
From why a program should begin to what comes next,
connect science and execution through evidence.
Identify opportunities worth pursuing.
AI compares opportunities against clinical needs, existing therapies and evidence to inform program selection and prioritization.
INPUT / EVIDENCE
Clinical needs · Landscape · Available evidence
OUTPUT / DECISION
Opportunity assessment, target product concept, go / no-go criteria
Connect disease questions to testable mechanisms.
Map indications, targets and direction of effect. Compare mechanisms, supporting findings and counterevidence.
INPUT / EVIDENCE
Disease biology · Target evidence · Direction of effect
OUTPUT / DECISION
Mechanistic hypotheses, target combinations and evidence gaps
Turn natural diversity into a candidate space.
Design candidates for natural-origin original molecules, global botanical drugs and innovative TCM drugs. Compare molecular, botanical, extract and formulation evidence.
INPUT / EVIDENCE
Botanicals · Chemical structures · Initial activity
OUTPUT / DECISION
Candidate priorities, design rationale and testable hypotheses
Design experiments that inform decisions.
AI organizes validation plans and compares activity, exposure and safety evidence against the original hypotheses.
INPUT / EVIDENCE
Candidate hypotheses · Activity and exposure · Safety signals
OUTPUT / DECISION
Experiment proposals, updated evidence and progression rationale
Build a developable quality foundation.
Organize research on source materials, characterization, processes and quality attributes. Identify consistency and scale-up risks.
INPUT / EVIDENCE
Chemical profiles · Process data · Quality studies
OUTPUT / DECISION
Quality framework, process risks and control strategies to validate
Connect evidence with a development path.
Integrate nonclinical, pharmacokinetic and clinical evidence to develop research plans. Accountable experts review clinical implementation and safety decisions.
INPUT / EVIDENCE
Nonclinical results · PK/PD · Clinical evidence
OUTPUT / DECISION
Translational questions, draft study plans and stage reviews
Understand key boundaries early.
Bring novelty, patent information and registration pathways into research discussions with traceable materials for professional assessment.
INPUT / EVIDENCE
Technical concepts · Published patents · Regulatory materials
OUTPUT / DECISION
Novelty leads, pathway questions and review packages
Connect execution with the next decision.
Build AI-driven program management around milestones, resources, dependencies and risks. Trigger reassessment when new evidence arrives.
INPUT / EVIDENCE
Milestones · Resources and dependencies · New evidence
OUTPUT / DECISION
Task priorities, risk signals and milestone reassessment
New evidence informs the next decision. Accountable teams review key experiments, clinical safety and development milestones.
Evidence is our shared language.
Connecting botanicals, compounds, targets and indications is a starting point. We ask where the evidence comes from, whether direction of effect is clear, how experimental conditions apply, and what remains unknown. Predictions and validated findings remain distinct.
Connect selection with execution.
Program selection compares opportunity and risk. Candidate design produces testable hypotheses. Experiment planning prioritizes questions that matter. Program management updates actions around resources, dependencies and milestones.
New evidence keeps development learning.
An experiment may support a hypothesis or change a program’s direction. Connect new data with the original rationale, preserving counterevidence, versions and reasons for changes in a question–test–evaluate–decide cycle.
Clear decisions need clear accountability.
AI produces analyses, priorities and proposed actions. Scientific teams are responsible for experimental quality and interpretation. Responsible personnel and specialist teams review clinical implementation, safety, regulatory submissions and key programs. Every stage should define progression, further-evidence and stopping criteria.