Map target literature
Map target literature using the current research target and its supporting records.
RESEARCH TARGET / SCREENING DATASET / CANDIDATE EVIDENCE
AI agent for drug discovery research teams
Keep the target hypothesis connected to the screening evidence.
Map target literature, reconcile screening evidence and prepare discovery research packs—carried through by an autonomous AI agent that adapts to your work.
A specialised AI agent you can ask for more. Chat here on the site.
$249.00/month, including $50.00 in monthly credits. Monthly subscription. Before tax.
Specialised AI agentYour own report presetNo connection needed to begin
Research target workbenchSPECIALIST / 1142
YOUR SPECIALISED AI AGENT
One place for map target literature, reconcile screening evidence, prepare discovery research packs.
MADE FOR YOUR WORKDAYStart with work already on your desk
Your first useful assignment.
Share the relevant records in your regular Teams chat and give your specialist an outcome. No model or tool choices to make first.
Get my AI agent →YOU COULD ASK
“Build the target evidence pack, reconcile the screening results and prepare a prioritised research plan with traceable candidate reasoning.”
- Map target literature
- Reconcile screening evidence
- Prepare discovery research packs
A useful first result
An editable research target and candidate evidence register with traceable references, resolved discrepancies and specific next actions.
Example assignment, not a promised delivery time. Your agent works from the records and access you provide. Add connections when you want it to act in other systems.
A conversation at your desk
Build the target evidence pack, reconcile the screening results and prepare a prioritised research plan with traceable candidate reasoning.
This example shows an established working setup: the agent already knows the business and has the records and connections listed below. Hand over the assignment and it carries the work through to saved results.
Illustrative project · configured agent at work
A configured agent with your research target, screening dataset, candidate evidence records, current commitments and the connected accounts used to reconcile screening evidence.
- Map target literature
- Reconcile screening evidence
- Prepare discovery research packs
A working specialist can handle the project, with a configured team sharing the work where available. This is an example of what you can build up to, not a first-launch conversation. Timing and usage depend on the assignment.
- You · Drug discovery research teams
Build the target evidence pack, reconcile the screening results and prepare a prioritised research plan with traceable candidate reasoning.
- Your AI agent · AI Agent Store
Two assays use incompatible concentration units and one candidate lacks provenance. I am resolving those before comparing the results.
Work in progress - You · Drug discovery research teams
Keep assay conditions visible and separate predicted activity from measured findings.
- Your AI agent · AI Agent Store
The discovery pack includes comparable screening evidence, documented candidate rationale and a research plan with unresolved validation needs. Your working files and dashboard reflect the recorded outcomes.
Later · delivery and saved results
RESEARCH TARGET / SCREENING DATASET / CANDIDATE EVIDENCE
A working brief for your AI agent.
Track target identifiers, assay conditions, compound provenance and candidate rationale. Preserve negative findings and distinguish computational predictions, assay observations and later validation stages. Your specialist maintains these working records in regular Teams as the assignment develops.
AI
Your AI agentPrepared for drug discovery research teams
Research target and candidate evidence registerEXAMPLE, NOT LIVE ACTIVITY
An example record for research target, screening dataset, candidate evidence, evidence criteria.
01Research target02Screening dataset03Candidate evidence
The useful next step
Map target literature
Map target literature using the current research target and its supporting records.
The details your specialist follows
Working records for drug discovery research teams
Follow each research target through the working record. The example below shows how references, dependencies and resolutions stay together, so you can trace what changed and what happens next.
Keep an eye on: changes in screening dataset, missing candidate evidence evidence and unresolved research target dependencies.
Illustrative working document · your records start empty
| Research target | Dependency to follow | Evidence of completion |
|---|---|---|
| OPPORTUNITY116-104 | Map target literature | Assay record discrepancies resolved |
| OPPORTUNITY116-118 | Reconcile screening evidence | Candidate evidence reviews completed |
| OPPORTUNITY116-126 | Prepare discovery research packs | Discovery research milestones met |
Active research target records3
Evidence criteria across the planned work320 evidence criteria
Outcomes with supporting evidence2
Illustrative project figures. Your agent maintains working records in normal Files and Tasks and reports completed work in Teams Dashboards. Ask it to adapt those records and reports as your workflow changes.
From enquiry to a useful next step
Research target and candidate evidence register. Your way of working.
01
Brief and source records received
Add a job reference and the details you already know.
02
Map target literature
Research target and Screening dataset, ready to check.
03
Reconcile screening evidence
Give your specialist the outcome and let it carry the work forward.
04
Outcome evidence verified
Keep working records in Files and Tasks.
05
Prepare discovery research packs
Keep the job and its next steps together.
Ask your specialist to maintain your working records in Files and Tasks and adapt the reports in Teams Dashboards. These are the regular platform tools, prepared for your business. Connections to other systems are optional.
A small tool for a real job
Evidence criteria across the planned work. Before the back-and-forth.
Watch for changes in screening dataset, missing candidate evidence evidence and unresolved research target dependencies. Investigate the cause, carry useful follow-through to completion and improve the saved method when it helps.
Try the numbers, then bring the confirmed brief to your specialist. Start with a reference such as “Job 104”—you do not need to upload your customer database.
- Research target
- Screening dataset
- Candidate evidence
- Evidence criteria
Try your own numbers
Evidence criteria across the planned work
Planning arithmetic from your inputs. Your specialist can turn the confirmed details into a working brief. This is not a quote or capacity commitment.
Built around the details that matter
Map target literature. With the right questions already in mind.
Early discovery research evidence, distinct from clinical trials, drug prescribing and general biomedical project administration.
Reconcile screening evidence
Reconcile screening evidence while keeping screening dataset changes traceable.
Prepare discovery research packs
Prepare discovery research packs with checked candidate evidence evidence and editable working files.
Prepared for your business
The numbers behind your ai agent for drug discovery research teams.
Six useful measures, ready in your regular Teams dashboard. Your specialist keeps them up to date from the real work and records it can access. Ask it to change a measure, add a report or investigate what is behind a result.
Recorded cases · outcomes
Discovery evidence packs delivered
What this measures
Count one distinct observed outcome for “Discovery evidence packs delivered”, linked to its research target and stable completion-event ID. Use the real completion timestamp and supporting candidate evidence reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Assay record discrepancies resolved
What this measures
Count one distinct observed outcome for “Assay record discrepancies resolved”, linked to its research target and stable completion-event ID. Use the real completion timestamp and supporting candidate evidence reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Candidate evidence reviews completed
What this measures
Count one distinct observed outcome for “Candidate evidence reviews completed”, linked to its research target and stable completion-event ID. Use the real completion timestamp and supporting candidate evidence reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Average · hours
Complete evidence receipt to discovery review
What this measures
Measure “Complete evidence receipt to discovery review” in elapsed hours using the actual start and end timestamps for the same research target. Store one duration per completed observation with source references and timezone. Exclude pending cases, missing timestamps, reversed intervals and illustrative values. Never substitute a current status or sum recurring snapshots.
Observed rate · %
Discovery research milestones met
What this measures
Completed commitments assessed for “Discovery research milestones met”, linked to stable research target references. Numerator: on_time; denominator: on_time plus late from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Compare actual completion with the original agreed deadline and timezone; never move the deadline to improve the result. Corrections replace the original event instead of counting twice.
Observed rate · %
Candidate records meeting provenance criteria
What this measures
Completed assessments for “Candidate records meeting provenance criteria”, linked to stable research target references. Numerator: passed; denominator: passed plus failed from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Apply the documented requirement or acceptance checklist and retain the actual supporting evidence; do not infer a pass from an administrative status. Corrections replace the original event instead of counting twice.
Your reports start empty. Figures reflect observed work in the selected period; missing records are not treated as measured zeroes. Current jobs, schedules and stock stay in your working files and tasks. You and your agent can adapt the dashboard as your business changes.
Keep the tools that already work
Specialised in your trade. Open to your next idea.
Your AI agent starts with the details of your business already in mind. The three workflows are starting points. Ask it for help with another task in the same conversation.
An AI agent prepared for your business
The starting instructions, intake questions and working rules already match drug discovery research teams. Add your service list and one example of your tone when you are ready.
Give your agent the whole job
Give your agent an outcome, the access it needs and your budget. It handles routine decisions and follows through in your connected tools, asking when a missing business decision needs you. Set or change your own boundaries whenever you want.
Ask beyond the starting workflows
Your agent can take on a new assignment, build a new workflow, improve its saved methods and adapt its dashboard as your needs evolve. The starting examples do not limit its capabilities. Give your agent another task in plain language. What it can carry out depends on the tools you connect and the access you choose.
Recommended starting plan
Your AI agent for drug discovery research teams.
Begin with one specialist and a report preset for your business. After setup, your regular Agent Teams conversation opens. No tool selection before your first conversation.
$249.00/month
Capacity for 1 running agent · hosting throughout the month
Start my AI agent →A clear starting total
Your Agent plan is $199/month with hosting included. This recommended offer adds $50 in monthly Platform Credit funding, for $249/month.
$249.00 before tax today and monthly, including $50.00 in monthly Platform Credit funding.
Credits offer an easy start and unused credits roll over. You can switch to your own subscriptions or API keys anytime, with no Platform Credits needed for that AI work. Hosting is included within your agent’s resource allocation. Optional voice and other services use credits.
Hosting availability is checked before payment. Create your account, confirm your monthly subscription, then start chatting when your specialist is ready. Your instructions and dashboard are prepared automatically after payment.
Other plans and connection options →
Prefer your own AI accounts? Choose a plan without the extra $50 monthly credit funding using the options above. Changing AI access alone does not remove that funding. Automatic top-ups start off; buy credits or set top-ups and spending limits when needed. Keep the full Teams controls for models, connections and supported settings.
Before you begin
An AI agent for drug discovery research teams.
How does the agent keep research target work accurate?
Track target identifiers, assay conditions, compound provenance and candidate rationale. Preserve negative findings and distinguish computational predictions, assay observations and later validation stages. It keeps source references with the working files, checks the result and follows up on discrepancies. Ask it to adjust the method or report whenever your needs change.
Is this only for the three tasks shown?
No. This is an AI agent specialised for drug discovery research teams, with three prepared workflows to get you started. You can ask for writing, research, planning, analysis or another task in the same chat. It can make routine decisions and carry the work through using available tools. You set its boundaries; it asks when a real business decision or missing access needs you.
What happens after I subscribe?
After payment, we prepare your AI agent for drug discovery research teams, its workflows and your research target and candidate evidence register dashboard. Your regular Agent Teams chat opens when setup is ready. Files, Tasks, settings and billing work the same as for other customers. Reports start empty and count actual reported work; the examples above are illustrative.
Do I need to connect email or messaging?
No. Prepare your first brief and draft here. Connect external channels later when you want the specialist to work there.
Does this replace my existing business software?
Use it alongside your current tools for preparing work and organising jobs. Live stock, availability, sending and payment records require a verified connection or your own confirmation.
Choosing for a different business?
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