> ## Documentation Index
> Fetch the complete documentation index at: https://docs.revilico.bio/llms.txt
> Use this file to discover all available pages before exploring further.

# ADME Multi-Parameter Optimization

> Multi-Parameter Optimization for Developability: Solubility, ADME, and Toxicity

## **The Problem You Are Trying to Solve**

*“I have a set of lead compounds, and I want to optimize them across multiple developability dimensions, like solubility, ADME, toxicity, and related properties, without losing on-target potency.”*

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At the lead optimization stage, teams often face tradeoffs:

* A potent binder may be insoluble or poorly permeable
* A promising series may carry CYP liabilities, hERG risk, or DILI flags
* Small structural changes can meaningfully shift clearance, bioavailability, or safety

Multi-parameter optimization (MPO) is about balancing the full profile, not maximizing a single metric.

## **Solution**

This workflow uses a primary MPO loop that alternates between:

1. Designing new analogs with the right constraints
2. Scoring them across developability endpoints
3. Filtering for synthesize-ability and feasibility
4. Repeating until a balanced set emerges

The primary MPO chain on Revilico is: Baseline Profiling → Property Screening (Solubility + ADMET-AI) → Multi-Objective Molecular Optimization → Synthesis Feasibility (Retrosynthesis) → Iterate.

Binding engines can be integrated at any stage to ensure potency and selectivity are not sacrificed while optimizing developability.

## **What Data Do I Need to Provide?**

Required

* Lead structures (SMILES / CSV)

Recommended

* Your desired optimization targets (examples: “increase solubility, reduce CYP3A4 inhibition risk, maintain MW \< 500”)
* Optional hard constraints (avoid specific motifs, preserve pharmacophore features, keep series scaffold)

Optional

* Experimental solubility/clearance/tox data (if available)
* On-target structure/assay signals for potency anchoring

## **Workflow**

1. **Generate New Analogs Using Multi-Objective Molecular Optimization**

Design new molecules that improve multiple endpoints simultaneously. Use **Generative Chemistry → Molecular Optimization** to:

* Generate analogs from the existing leads (conservative or exploratory)
* Ensure that there is proper activity of your ligands to the target of interest during this process
* Encode MPO goals as a multi-component scoring function (e.g., solubility ↑, CYP risk ↓, hERG risk ↓, MW/LogP constraints, etc.)
* Apply diversity controls so you don’t get 1,000 near-duplicates

This step is the core MPO engine: sample → score → update → repeat, producing candidates increasingly aligned to your objective profile, resulting in a versioned library of optimized analogs ranked by multi-objective score

2. **Establish the MPO Baseline**

After generating new molecules, define the success criteria for the lead series. On Revilico, users typically:

* Run **ADMET-AI Analysis** to surface the major liabilities (toxicity flags, CYP inhibition/substrate risks, clearance risk, permeability proxies)
* Run **Compound Solubility** to understand baseline solubility trends across solvents and temperature
* You can run the **Ligand Membrane MD** Engine that will allow you to analyze permeability of the compound to different seeded cell membranes
* (Optional) Run **Conformer Search** or other quantum methods to understand flexibility and shape drivers that may impact permeability and exposure

This baseline informs:

* Which liabilities matter most
* Which constraints must remain fixed
* How aggressive optimization should be

This step provides a clear MPO objective (prioritized endpoints + acceptable ranges).

3. **Rapid Property Screening and Downselection**

Run fast, high-throughput filters to avoid spending time optimizing the wrong chemistry. Use:

* **ADMET-AI Analysis** to identify red flags early (toxicity, CYP panels, hERG/DILI risk, clearance risk, permeability)
* **Compound Solubility** to identify which leads have a solubility ceiling that must be addressed by design

At this stage, you are not looking for perfection, you are identifying:

* which leads are salvageable
* which liabilities are dominant
* which properties move together vs trade off

This gives a ranked lead shortlist + a prioritized “liability map” per compound.

4. **Interpret and Filter Results**

After the optimization run, identify candidates that are *balanced*, not just “good at one metric.” Common selection heuristics:

* Prefer compounds with consistent ADMET improvements (not one extreme gain paired with new liabilities)
* Avoid candidates that only score well due to compensation effects
* Maintain chemical diversity among finalists to reduce risk of series-level blind spots

Use:

* **ADMET-AI Analysis** (re-run on the new library) for confirmation
* **Compound Solubility** (re-run for top candidates) to validate solubility gains

This produces a short list of MPO-balanced candidates for synthesis planning

5. **Check Conformations, Electronic Risk, and Physical Plausibility (Optional)**

For finalists, deepen confidence before synthesis. Use:

* **Conformer Search** to ensure the compounds can adopt relevant 3D shapes without extreme strain
* **Geometry Minimization and Thermochemistry** to confirm stable geometries and rule out unstable/high-energy structures
* **Molecular Orbital Analysis (HOMO–LUMO)** for electronic reactivity signals (useful for flagging unstable/reactive chemistry)

This step supports “sanity checks” that catch hidden risks, giving physics-backed confirmation that finalists are structurally and electronically reasonable.

6. **Ensure Synthesizability with Retrosynthesis**

A candidate that cannot be made is not a lead. Use **Retrosynthesis** to:

* Propose ranked synthetic routes
* Flag candidates with unrealistic or costly synthesis pathways
* Prioritize compounds that are both *better* and *buildable*

This engine results in a buildable shortlist with actionable synthetic plans.

## **Results**

* A refined set of candidates improved across solubility, ADME, and toxicity dimensions
* Explicit tradeoff awareness (what improved, what worsened, and why)
* Higher confidence in which compounds are worth synthesis and assays
* A repeatable MPO loop you can run iteratively as new data arrives

## **Integration with Other Engines (Optional)**

If potency/selectivity must be enforced within the MPO loop, users can integrate:

* Docking / Virtual Screening to maintain binding hypotheses while improving developability
* Protein–Ligand MD to validate binding stability for optimized candidates
* ABFE/RBFE for high-confidence ranking among close analogs

This allows MPO to be developability-first without losing efficacy.

## **Why Revilico?**

Revilico enables MPO as a single connected loop:

* Generative design (**Molecular Optimization**) grounded in explicit scoring
* Fast developability screens (**ADMET-AI**, **Solubility**) to guide iteration
* Physical plausibility checks (**Conformer Search**, **QM engines**) for confidence
* Buildability validation (**Retrosynthesis**) to ensure real-world feasibility

This produces candidates that aren’t just more potent, but actually more likely to succeed in development.