Prerequisite: Load The Molecule From ChemrytIQ

Before opening ChemrytIQ-MCCP, search the molecule in ChemrytIQ by SMILES, InChI, molecule name, or CAS number. Confirm the correct molecule on the ChemrytIQ page, then open the required Chemryt app from that same molecule context so the selected structure is loaded into the app automatically.

What It Does

ChemrytIQ-MCCP is a client-side prioritization module that helps compare compounds with multiple criteria rather than a single score. It is intended for rank ordering and tradeoff review after search or prediction modules produce candidate data.

Criteria weighting

Balances potency, properties, toxicity, ADMET, novelty, safety, or project-specific priorities.

Compound ranking

Compares candidate molecules and shows which criteria drive their rank.

Decision trace

Keeps prioritization transparent so users can explain why a compound moved up or down.

Quick Tutorial

Collect candidates from ChemrytIQ search, QSAR, ToxPred, DeNovo, PCP, or other child modules.
Open MCCP and review the current compound set and available criteria.
Set weights to match the project goal, such as safety-first, potency-first, balanced developability, or EHS screen.
Run prioritization and inspect ranked compounds plus criterion-level drivers.
Adjust weights and rerun when the project question changes.
Use the top candidates for follow-up experiments, design review, or deeper modeling.

Main Areas

Area	What to review	When to use it
Inputs	Candidate structures, descriptors, prediction scores, toxicity flags, and source metadata.	Use to define the comparison set.
Weights	Project priorities and criterion importance.	Use to make the ranking match the scientific question.
Rank output	Compound order, score components, tradeoffs, and decision notes.	Use for shortlist selection.

ML Model / Computation Used

Model or method	What it predicts	Implementation details
Weighted multi-criteria scoring	Compound ranking from property, QSAR, toxicity, ADMET, novelty, safety, and project-fit criteria.	No standalone trained ML artifact was found for MCCP. Rankings are computed from user weights and upstream model/descriptor outputs.

Good Practice

MCCP is only as reliable as its inputs and weights. Record the weighting scheme and rerun prioritization when new assay or safety data arrives.

Reference Used

This Tutorial page mirrors the ChemrytIQ reference module: ChemrytIQ-MCCP.