---
title: "Screen ADMET"
description: "Screen ADMET: a screening recipe chaining admet_ai, deeppurpose."
tags:
  - recipes
  - screening
tools:
  - admet_ai
  - deeppurpose
generated: true
source: quickActionConfig:admet-complementary
---

> **Generated recipe**

> Seeded from a curated quick-action prompt. The prompt below runs the real
> routing — copy it, change the target, and run.

> **Recommended pairing**

> These tools are commonly run together — they cover complementary ground on the same input.

**Best fit:** screen ADMET — a screening goal.

**Try this prompt** — [open in a guest chat](/chat/new?guest=true&prompt=Fetch%20gefitinib%2C%20erlotinib%2C%20and%20osimertinib%20from%20PubChem%2C%20run%20ADMET-AI%20on%20all%20three%2C%20then%20dock%20each%20against%20CYP3A4%20(PDB%3A%204NY4)%20with%20DiffDock%20to%20predict%20CYP%20binding%20orientation%20and%20inhibition%20mechanism%20-%20load%20ADMET-AI%20CYP%20profiles%20and%20DiffDock%20poses%20into%20the%20sandbox%20and%20use%20matplotlib%20to%20generate%20a%20comparative%20CYP%20inhibition%20dashboard%20with%202D%20depictions%20and%20binding-mode%20thumbnails%20via%20py3Dmol%2C%20select%20the%20best%20candidate%20for%20co-administration%20with%20a%20CYP3A4%20substrate%2C%20and%20outline%20the%20clinical%20DDI%20study%20design%3F).

> Fetch gefitinib, erlotinib, and osimertinib from PubChem, run ADMET-AI on all three, then dock each against CYP3A4 (PDB: 4NY4) with DiffDock to predict CYP binding orientation and inhibition mechanism - load ADMET-AI CYP profiles and DiffDock poses into the sandbox and use matplotlib to generate a comparative CYP inhibition dashboard with 2D depictions and binding-mode thumbnails via py3Dmol, select the best candidate for co-administration with a CYP3A4 substrate, and outline the clinical DDI study design?

_A real `admet-complementary` quick-action prompt — open it in a guest chat, then swap in your own compound or target._

## The prompt

```text
Fetch gefitinib, erlotinib, and osimertinib from PubChem, run ADMET-AI on all three, then dock each against CYP3A4 (PDB: 4NY4) with DiffDock to predict CYP binding orientation and inhibition mechanism - load ADMET-AI CYP profiles and DiffDock poses into the sandbox and use matplotlib to generate a comparative CYP inhibition dashboard with 2D depictions and binding-mode thumbnails via py3Dmol, select the best candidate for co-administration with a CYP3A4 substrate, and outline the clinical DDI study design?
```

## What each tool contributes

- [admet_ai](/docs/admet-ai) — 50+ ADMET endpoints (comprehensive panel)
- [deeppurpose](/docs/deeppurpose) — 20 deep-learning ADMET endpoints (CYP panel, hERG, BBB, Caco-2, AMES, LD50, DILI) + DTI/DDI predictions

## Why these tools pair

Comprehensive ADMET coverage: ADMET-AI provides 50+ endpoints while DeepPurpose adds 20 deep-learning ADMET endpoints (CYP1A2/2C9/2C19/2D6/3A4, hERG, AMES, BBB, Caco-2, lipophilicity, solubility, clearance, half-life, LD50, DILI) plus DTI and DDI predictions

## Data flow

Every tool operates on the same input: `smiles_array`.

## Read next

- [Which tool?](/docs/which-tool) — match a single goal to a tool.
- [Workflows](/docs/workflows) — every workflow and recipe.
- [How the orchestrator picks tools](/docs/how-orchestration-works) — the same chain from a plain-language goal.