Small-Molecule Design
Turn project data into testable design hypotheses and a balanced next compound set
Combine reference-guided design, controlled medicinal-chemistry edits, hierarchical filtering, validated structure-based evidence, ADMET and project-adaptive prioritization.
Bring your current series. Define the next chemistry cycle.
PROJECT DECISION
Actual Platform Interface
Turn project liabilities into explicit optimization targets
Define the primary development risk, secondary liabilities, PK/PD requirements and ADMET objectives that guide the next medicinal chemistry cycle.
Medicinal Chemistry Workflow
Design hypotheses first. Computation supports the decision
1Series context
2Design hypothesis
3Controlled analogs
4Hierarchical filters
5Structure & ADMET
6Portfolio selection
7Synthesis/test plan
Hierarchical Filtering
Stop low-value candidates before they consume expensive resources
SMILES, valence, elements, duplicates, unstable/reactive motifs
Stop invalid candidates
MW, LogP, TPSA, HBD/HBA, rings, alerts, synthetic accessibility
Keep series-compatible chemistry
Absorption, metabolism, clearance, cardiac, genotoxicity and liver-risk signals
Identify rescue or stop decisions
Validated receptors, multiple conformations, key interactions and poses
Form binding and selectivity hypotheses
Pareto trade-offs, diversity, novelty, series coverage and patent-space signals
Select a balanced compound set
Design Hypotheses
Every analogue should answer a project question
Increase potency while preserving the known pharmacophore
Reduce lipophilicity without losing permeability
Address a metabolic soft spot with a traceable edit
Improve selectivity using cross-receptor evidence
Expand structural distance while retaining medicinal-chemistry plausibility
Project-Adaptive Optimization
Preserve useful trade-offs and learn from project history
The customer-facing result is a balanced portfolio; Pareto analysis, diversity controls, generator selection and DMTA feedback operate underneath.
Multi-objective evidence
Balance binding, ADMET, physicochemical properties, synthesis, novelty and confidence.
Pareto-based selection
Retain strong trade-offs instead of collapsing all evidence into one opaque score.
Series diversity
Avoid convergence on one chemotype and preserve backup hypotheses.
DMTA learning
Use computational and experimental history to guide the next round.
From molecular ideas to the next experimental decision
Discuss a focused project review, pilot scope or complete optimization program with MedMap.
Contact MedMap