Receptor.AI Unveils Advanced ADME-Tox Prediction Model

Optimizing preclinical trials with advanced AI predictions for pharmacokinetics and drug safety

Receptor.AI introduces a groundbreaking ADME-Tox prediction model, transforming efficiency in AI-assisted drug discovery. ADME-Tox—key to pharmacokinetic studies—determines a compound's viability in clinical stages. Traditionally, assessing these parameters is laborious and costly, highlighting the need for efficient early-stage screening.

The company's comprehensive ADMET module employs a multi-parameter AI model to predict over 40 ADME-Tox endpoints and 20+ physicochemical attributes. Applied during secondary screening, this module filters compounds, focusing on those most likely to succeed in further drug development phases.

Figure 1. The architecture of the multitask ADME-Tox properties prediction model

Figure 1 outlines the architecture of this multitask ADME-Tox prediction model. Trained on diverse datasets from ChEMBL and ToxCast, among others, the model uses a Graph Neural Network (GNN) to process molecular graphs, leveraging multi-task learning to optimize predictions across numerous parameters. This innovative approach ensures a broad applicability and enhanced prediction accuracy.

Receptor.AI's meticulous training and standardization protocol ensures high-quality predictions. The model's performance, detailed through binary classification and regression analyses, demonstrates its capability to reliably forecast ADME-Tox outcomes. Although direct comparisons are challenging due to scarce public benchmarks, Receptor.AI's model consistently exhibits superior performance, suggesting significant improvements in compound selection efficiency for preclinical trials.

By leveraging AI to streamline the initial phases of drug discovery, Receptor.AI's ADME-Tox prediction model not only expedites the development process but also paves the way for creating safer, more effective drugs. This model represents a pivotal advancement in utilizing AI for enhancing drug safety and pharmacokinetics predictions, underscoring the transformative potential of AI in drug development.