Oral Integrin Target Designer

AI-driven target identification and chemistry design application for discovering selective, orally bioavailable small-molecule inhibitors against challenging integrin targets in inflammatory disease programs.

The Problem

“Design selective, orally bioavailable small-molecule integrin inhibitors faster for inflammatory disease programs”

Organizations face these key challenges:

1

Integrin targets are challenging for achieving both selectivity and oral bioavailability

2

Medicinal chemistry design space is too large for manual exploration

3

Assay, ADME, and selectivity data are fragmented across systems

4

Tradeoffs between potency and developability are hard to optimize simultaneously

Impact When Solved

Shortens DMTA cycles by prioritizing higher-value compounds before synthesisImproves multi-parameter optimization across potency, selectivity, permeability, solubility, and metabolic stabilityExpands exploration beyond known analog series into novel chemotypesReduces wet-lab cost by filtering low-probability compounds earlier

The Shift

Before AI~85% Manual

Human Does

  • •Review literature, prior SAR, structural biology, and assay results to choose promising integrin chemotypes
  • •Propose analogs and set medicinal chemistry priorities across potency, selectivity, and oral exposure goals
  • •Decide which compounds to synthesize and advance into in vitro, selectivity, and DMPK studies
  • •Interpret assay and ADME results and adjust hypotheses for the next DMTA cycle

Automation

  • •Run standard docking and property calculations on selected compounds
  • •Aggregate assay, SAR, and ADME data into summary views for team review
  • •Flag basic developability risks from predefined computational filters
With AI~75% Automated

Human Does

  • •Set target product profile and approve optimization priorities across potency, selectivity, oral bioavailability, and safety
  • •Review AI-ranked compounds and decide which designs to synthesize and test
  • •Handle exceptions where AI proposals conflict with medicinal chemistry judgment, project strategy, or assay evidence

AI Handles

  • •Combine historical SAR, assay, and ADME evidence to score compounds against the oral integrin profile
  • •Generate and rank new synthetically plausible analogs under multi-parameter constraints
  • •Prioritize synthesis and testing queues based on predicted potency, selectivity, permeability, solubility, and metabolic stability
  • •Monitor incoming assay results, update recommendations, and surface high-value next-step experiments

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence95%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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