Scientist working in a drug discovery laboratory

AI-driven Drug Optimization

Design the next compound set with validated structural context, multi-parameter evidence and project-specific learning

From receptor validation and molecular design to ADMET, translational exposure and DMTA decisions, MedMap helps discovery teams decide what to make next, what to stop and why.

Bring the current project question. Define the most useful next decision workflow.

Ranked, diverse compound set

Ranked, diverse compound set

Balanced candidates across distinct design hypotheses

Candidate-specific rationale

Candidate-specific rationale

Strengths, liabilities, edits and uncertainty

Next-cycle decision plan

Next-cycle decision plan

What to synthesize, test, stop and validate

Four Decision Services

Start with the question your project needs to answer

Each service can be used independently or connected within one traceable discovery program.

1.

Small-Molecule Design

What should we design and synthesize next?

Project-specific generation, hierarchical filtering and adaptive portfolio selection.

2.

Receptor Workbench

Can the structural protocol support ranking?

Multi-receptor preparation, reference-ligand redocking and explicit diagnostics.

3.

Translational PK/PD

Will compound properties translate into exposure and effect?

Systemic/tumor exposure, target occupancy and efficacy scenarios.

4.

ADC Systems Optimization

How should the complete ADC construct be optimized?

Target, antibody, linker, payload, DAR, exposure and safety-window optimization.

Discovery Decision Workflow

From project question to the next experimental cycle

  1. Project question
    1Project question
  2. Validated context
    2Validated context
  3. Design hypothesis
    3Design hypothesis
  4. Hierarchical filters
    4Hierarchical filters
  5. Multi-objective selection
    5Multi-objective selection
  6. Translational scenarios
    6Translational scenarios
  7. DMTA decision
    7DMTA decision

Why MedMap

A decision architecture—not a collection of isolated AI models

Validate receptors and protocols before optimization

Translate molecular properties into exposure and target-engagement scenarios

Keep design hypotheses and compound-series context visible

Use project history to refine the next DMTA cycle

Balance potency, ADMET, synthesis, diversity and novelty

Separate platform outputs from research, managed and legal review boundaries

From molecular ideas to the next experimental decision

Discuss a focused project review, pilot scope or complete optimization program with MedMap.

Contact MedMap