ADMET-AI vs ADMETlab 3.0 vs SwissADME: Which ADMET Prediction Tool Should You Use?

Why ADMET Prediction Matters in Drug Discovery

Nearly 40 percent of drug candidates fail in clinical trials due to poor pharmacokinetic properties or unexpected toxicity. ADMET prediction tools aim to catch these failures early by computationally estimating how a molecule will behave in the human body: how well it is absorbed, where it distributes, how it is metabolized by liver enzymes, how quickly it is excreted, and whether it triggers toxic effects.

Running ADMET screens before synthesis saves months of wet-lab work and millions of dollars in development costs. Three tools dominate this space today: ADMET-AI, ADMETlab 3.0, and SwissADME. Each takes a different approach to the same problem, and the right choice depends on your workflow, throughput needs, and how you plan to integrate predictions into your pipeline.

ADMET-AI: Machine Learning Across 41 Endpoints

ADMET-AI is an open-source deep learning framework trained on the Therapeutics Data Commons (TDC) benchmark datasets. Version 2 covers 41 ADMET endpoints spanning absorption, distribution, metabolism, excretion, and toxicity, making it one of the most comprehensive single-model ADMET tools available. It uses graph neural networks and molecular fingerprints to generate predictions from SMILES input.

• Developer: Swanson et al. (open source)
• Endpoints: 41 ADMET properties from TDC benchmarks
• Input: SMILES strings (single or batch)
• Access: Python package, local deployment, or SciRouter API
• License: open source, commercial use permitted
• Strengths: broad endpoint coverage, batch processing, API-friendly

ADMET-AI is particularly well-suited for high-throughput workflows where you need consistent predictions across dozens of properties for large compound libraries. Its programmatic interface makes it straightforward to integrate into automated screening pipelines.

ADMETlab 3.0: Comprehensive Web Platform

ADMETlab 3.0 is developed by the Zeng lab at Central South University and provides ADMET predictions through a feature-rich web platform. It covers a broad set of pharmacokinetic and toxicity endpoints and includes physicochemical property calculations, medicinal chemistry filters, and visualization tools in a single interface.

• Developer: Zeng Lab, Central South University
• Endpoints: 50+ ADMET and physicochemical properties
• Input: SMILES, SDF files, or drawn structures
• Access: web interface only (admetlab3.scbdd.com)
• License: free for academic use, no public API
• Strengths: extensive property coverage, interactive visualizations, medicinal chemistry filters

ADMETlab 3.0 excels as an interactive exploration tool for medicinal chemists who want to examine individual compounds in depth. Its web-based radar plots and property summaries provide immediate visual feedback on a compound's drug-likeness profile. The lack of a public API, however, limits its use in automated pipelines.

SwissADME: The Established Standard

SwissADME is developed by the Swiss Institute of Bioinformatics and has been a standard reference tool in computational medicinal chemistry since its release. It focuses on physicochemical descriptors, pharmacokinetic estimates, drug-likeness rules, and medicinal chemistry friendliness, with clear visual output through its BOILED-Egg model for gastrointestinal absorption and blood-brain barrier penetration.

• Developer: Swiss Institute of Bioinformatics (SIB)
• Endpoints: physicochemical properties, lipophilicity, pharmacokinetics, drug-likeness, medicinal chemistry
• Input: SMILES strings
• Access: web interface only (swissadme.ch)
• License: free for all users, no API
• Strengths: trusted benchmark, BOILED-Egg visualization, Lipinski/Veber/Ghose filter coverage

SwissADME remains the go-to tool for quick drug-likeness checks and is widely cited in published literature. Its main limitations are the lack of programmatic access and a narrower set of toxicity predictions compared to newer tools.

Feature Comparison

Endpoint Coverage

ADMET-AI v2 predicts 41 endpoints with a focus on TDC-benchmarked ADMET properties. ADMETlab 3.0 offers the widest coverage with over 50 properties including systemic toxicity endpoints and ecological toxicity. SwissADME focuses on physicochemical and pharmacokinetic properties with strong drug-likeness rule coverage but fewer explicit toxicity predictions.

Programmatic Access

ADMET-AI is the only tool among the three with a Python package and API access (available through SciRouter). ADMETlab 3.0 and SwissADME are web-only tools. For any workflow involving more than a handful of molecules, the ability to call an API programmatically is a significant advantage.

Batch Processing

ADMET-AI handles batch predictions natively, processing hundreds of SMILES in a single request. ADMETlab 3.0 supports file uploads (SDF) for moderate batch sizes through its web interface. SwissADME allows multiple SMILES entry but is designed primarily for small sets of compounds rather than large-scale screening.

Speed

ADMET-AI returns predictions for a single compound in under one second via API and can process batches of 1,000 molecules in minutes. ADMETlab 3.0 web predictions typically return within a few seconds per compound. SwissADME response times are similar for individual queries but lack the throughput for large libraries.

When to Use Each Tool

• Use ADMET-AI when you need programmatic ADMET screening in an automated pipeline, when processing large compound libraries, when you want consistent predictions across 41 endpoints, or when integrating ADMET checks into a CI/CD-style drug discovery workflow
• Use ADMETlab 3.0 when you want the broadest property coverage in a single session, when you need interactive visualizations for presentations or reports, or when evaluating individual lead compounds in depth during medicinal chemistry optimization
• Use SwissADME when you need a quick drug-likeness check against established rules (Lipinski, Veber, Ghose), when you want the BOILED-Egg permeability visualization, or when citing ADMET predictions in academic publications where SwissADME is the accepted standard

Running ADMET-AI via SciRouter API

Here is a complete example screening a compound for ADMET properties using the SciRouter ADMET-AI endpoint:

import requests

API_KEY = "sk-sci-your-api-key"
BASE = "https://api.scirouter.ai/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Screen a drug candidate for ADMET properties
response = requests.post(
    f"{BASE}/chemistry/admet",
    headers=headers,
    json={
        "smiles": "CC(=O)Oc1ccccc1C(=O)O",  # Aspirin
    }
)

result = response.json()

# Key ADMET endpoints
print(f"Caco-2 permeability: {result['caco2_permeability']}")
print(f"CYP3A4 inhibitor: {result['cyp3a4_inhibition']}")
print(f"hERG liability: {result['herg_inhibition']}")
print(f"Oral bioavailability: {result['oral_bioavailability']}")
print(f"Plasma protein binding: {result['ppb']}")
print(f"Half-life class: {result['half_life']}")
print(f"AMES toxicity: {result['ames_toxicity']}")
print(f"LD50: {result['ld50']}")

For batch screening, pass a list of SMILES to process an entire compound library in one request:

# Screen multiple compounds in one call
response = requests.post(
    f"{BASE}/chemistry/admet",
    headers=headers,
    json={
        "smiles": [
            "CC(=O)Oc1ccccc1C(=O)O",       # Aspirin
            "CC(C)Cc1ccc(cc1)C(C)C(=O)O",   # Ibuprofen
            "c1ccc2c(c1)cc(=O)oc2c3ccccc3",  # Warfarin
        ]
    }
)

results = response.json()["results"]
for compound in results:
    print(f"{compound['smiles']}: hERG={compound['herg_inhibition']}, "
          f"CYP3A4={compound['cyp3a4_inhibition']}")

Conclusion

All three tools serve the same fundamental goal — predicting whether a molecule will behave well as a drug — but they differ in accessibility, scope, and integration. SwissADME remains the trusted standard for quick checks and academic citations. ADMETlab 3.0 offers the richest interactive experience with the broadest endpoint coverage. ADMET-AI provides the programmatic access and batch throughput that modern drug discovery pipelines demand.

For teams building automated screening workflows, ADMET-AI via SciRouter gives you 41 ADMET endpoints through a single API call with no infrastructure to manage. Combine it with molecular property calculations and drug-likeness scoring to build a complete compound profiling pipeline.