Developability
ADMET prediction
Review ADME-Tox endpoints, physicochemical descriptors, structural liabilities, confidence, and applicability.
Explore ADMET prediction →Connected computational drug discovery
One evidence model across the discovery cycle.
Move from target and hit discovery through molecular design, developability, synthesis planning, and DMTA review without separating each calculation from the molecule, inputs, and scientific context that produced it.
Hit discoveryLead optimizationADME-ToxSBDDLigand-based designDMTA
Solution directory
Start with the scientific question, then carry the evidence forward.
Each solution has a distinct decision boundary. MolexIO keeps measured values, calculated descriptors, predictions, simulations, and missing evidence visibly separate while allowing them to inform the same candidate record.
Lead optimization
ADMET optimization
Compare a seed with proposed analogs, inspect structural changes, and balance endpoint desirability without hiding trade-offs.
Explore ADMET optimization →Hit discovery
Docking and virtual screening
Prepare a target, define the screening funnel, rank candidates, and inspect poses and interactions.
Explore docking and screening →Ligand-based design
Analog generation and screening
Search by molecular similarity and propose series-aware changes around a known ligand or lead.
Explore ligand-based design →Structure-based design
SBDD and pocket design
Resolve target readiness, inspect binding pockets, and turn structural hypotheses into reviewable candidate ideas.
Explore SBDD →Target intelligence
Target discovery and selectivity
Connect disease context, target identity, tractability, structural readiness, and molecule-level off-target evidence.
Explore target intelligence →Makeability
Retrosynthesis and supply
Review proposed disconnections, route caveats, and available compound supply as separate evidence streams.
Explore synthesis and supply →Affinity refinement
Free energy perturbation
Plan and interpret RBFE or ABFE calculations within an explicitly qualified structural series.
Explore FEP →3D ligand evidence
Shape and pharmacophore
Compare conformers, shape overlap, feature agreement, and consensus evidence around a reference ligand.
Explore 3D ligand screening →Learning cycle
DMTA and active learning
Join design choices, assay outcomes, model updates, and next-compound selection across iterative cycles.
Explore DMTA →Decision analysis
MPO scoring
Make desirability functions, weights, missing values, and what-if trade-offs inspectable.
Explore MPO →Predictive modeling
QSAR model workshop
Curate target-bound datasets, validate models, define applicability, and keep versions attached to predictions.
Explore QSAR modeling →Pharmacognosy
Natural-product discovery
Trace plants, compounds, activities, targets, and diseases back to source evidence.
Explore natural products →Shared foundation
Different methods. One reviewable project record.
Identity before inferenceMolecules, targets, structures, and source records remain addressable across workflows.
Provenance with every resultInputs, engine settings, artifacts, and evidence type stay attached to the output.
Boundaries made explicitA prediction or simulation is not presented as measured potency, safety, or experimental feasibility.
Map MolexIO to your current discovery workflow.
Bring the scientific question, available evidence, and decision you need to make. We will focus the demonstration on the relevant workflow and its limits.