Introduction

In the fast-paced world of drug discovery, AI-enabled prediction of Absorption, Distribution, Metabolism, and Excretion (ADME) properties for potential drug candidates is a game-changer. At Zscale Labs™, we're pioneering a novel approach that combines molecular fingerprinting with Hyperdimensional Computing (HDC) to create a powerful predictive model for ADME properties.

By leveraging Zscale Labs™ ADME Properties Prediction Model, drug discovery researchers and scientists may enjoy:

• Rapid Screening

• Reducing Experimental Costs

• Optimizing Lead Compounds

• Balancing Efficacy and ADME

• Predicting Specific ADME Properties

• Scalability and Integration with Other Models

  
  

Zscale Labs™ Model Architecture

Our ADME Properties Prediction Model features include:

Molecular Representation:

• FP4 Fingerprints: A 307-bit fingerprint based on SMARTS patterns, capturing diverse structural features.

• MACCS Fingerprints: A 166-bit fingerprint focusing on functional groups and atom types.

Feature Selection:

• Using Information Gain (IG), Zscale Labs™ selects the most informative bits from the polled fingerprints, reducing noise and focusing on the most relevant molecular features.

Machine Learning with HDC:

• Our Support Vector Machine (SVM) classifier, powered by HDC, excels in handling high-dimensional data, achieving complex decision boundaries with precision.

  
  

Model Performance

We're thrilled to share our model's impressive metrics:

• Accuracy: 88.05%

• Precision: 88.44%

• Recall: 97.14%

• AUC: 84.83%

These results underscore our model's strength, particularly its high recall, which highlights its sensitivity in identifying compounds with desired ADME properties.

Benefits of Advanced AI ADME Detection

By leveraging Zscale Labs™ ADME Properties Prediction Model, drug discovery researchers and scientists may enjoy:

• Rapid Screening – Our model's high accuracy and recall enable swift screening of large compound libraries, expediting the early stages of drug discovery.

• Reducing Experimental Costs – Accurate in silico predictions reduce the number of compounds needing synthesis and experimental testing, saving substantial costs.

• Optimizing Lead Compounds – Our model guides medicinal chemists in optimizing lead compounds by identifying structural features impacting ADME properties.

• Balancing Efficacy and ADME Properties – By considering both efficacy and ADME properties, Zscale Labs™ helps you identify compounds that balance potency and drug-likeness.

• Predicting Specific ADME Properties – Beyond binary classification, our model can be extended to predict specific properties like oral bioavailability, blood-brain barrier penetration, or metabolic stability.

• Scalability and Integration with Other Models – Our ADME prediction model integrates seamlessly with other in-silico models, creating a much more comprehensive and scalable drug discovery pipeline.

To see more details on HDC, click here.

Experience the Future of AI-Powered Drug-Discovery!

At Zscale Labs™, our innovative blend of machine learning and HDC for ADME Property Prediction marks a significant leap in drug discovery. By rapidly and accurately predicting ADME properties, we accelerate drug development, cut costs, and boost the chances of identifying successful drug candidates. As we refine and expand our model, it promises to become an invaluable tool in the pharmaceutical industry's quest for safer, more effective medicines.

For more information or to schedule a demo of our groundbreaking technology, visit Zscale Labs™ and follow us on LinkedIn for the latest updates and insights in AI-powered healthcare solutions.

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