A New Milestone in AI-Powered ADMET Profiling

SMAG is proud to unveil the launch of its P‑Glycoprotein (PGP) Substrate Prediction Tool, now integrated into our platform’s ADMET module. This machine learning-driven model marks a new step toward accurate, fast, and interpretable drug toxicity prediction—a crucial component of modern AI in drug development.

Predicting PGP substrate activity plays a critical role in understanding drug absorption, distribution, and resistance, especially in early-stage compound screening. With this tool, SMAG empowers researchers to assess molecular behavior more reliably—reducing risk and speeding up the development cycle.

What Is P‑Glycoprotein and Why It Matters?

P‑Glycoprotein (PGP) is a membrane-bound transporter protein responsible for pumping drugs and xenobiotics out of cells. It can significantly affect:

  • Drug bioavailability
  • Blood-brain barrier permeability
  • Multidrug resistance (MDR)

Identifying whether a compound is a PGP substrate is vital to ensure successful absorption, reduced toxicity, and optimized pharmacokinetics.

Powered by AI: What’s Under the Hood?

The SMAG PGP Substrate Prediction Tool is powered by a high-performance Random Forest classifier. It’s trained using Morgan fingerprints from SMILES strings—removing the need for any external descriptors.

Key Highlights:

  • Binary classification optimized for precision
  • Built entirely on molecular fingerprints for transparency
  • Fast and scalable for high-throughput screening workflows

Model Performance at a Glance

Metric Value
Accuracy0.94
ROC-AUC0.9502
MCC0.8736
Sensitivity0.9353
Specificity0.9398
F1 Score0.9427

These performance benchmarks place our tool ahead of industry-standard options in predictive toxicology.

Outperforms Leading Tools

SMAG’s latest ADMET feature surpasses the capabilities of several well-known software tools, including:

  • ADMET‑lab 3.0
  • ADMET‑SAR 3.0
  • Helix‑ADMET
  • Swiss‑ADME
  • vNN Web Service

With superior predictive power and no need for proprietary descriptors, SMAG offers a uniquely scalable solution in cheminformatics and virtual screening.

Designed for Scientists, Powered by AI

Whether you’re in early-stage discovery, lead optimization, or toxicity filtering, the SMAG PGP Substrate Predictor enhances confidence in your screening pipeline. It helps reduce downstream failures and supports better decision-making with explainable outputs and efficient model interpretability.

Explore how SMAG uses AI in drug discovery to redefine early-phase development.

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Frequently Asked Questions (FAQs)

PGP regulates the efflux of drugs from cells, impacting how well a drug is absorbed and distributed. Identifying PGP substrates helps prevent drug failure due to poor bioavailability or resistance. Learn more from the NIH.

AI models can learn patterns from vast chemical datasets to predict toxicity outcomes faster and more accurately than traditional methods. This leads to safer, more cost-effective drug pipelines. Read more from FDA.gov.

With an F1 score of 0.9427 and ROC-AUC of 0.9502, SMAG’s tool outperforms many industry-standard platforms—backed by transparent methodology and high interpretability.

Yes. It’s designed specifically for early-stage compound evaluation, reducing reliance on wet-lab validation by highlighting ADMET liabilities upfront.

No. The model runs exclusively on SMILES-derived Morgan fingerprints, making it easier, faster, and reproducible for non-specialist users.

Final Thoughts

SMAG’s new ADMET P‑Glycoprotein Substrate Prediction Tool represents a major leap in AI-driven drug development. Built on scientifically validated techniques and focused on speed, accuracy, and accessibility, it’s a must-have for anyone optimizing their drug pipeline.

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