Method Profile

Experimental Benchmark of ADME@NCATS Predictions in Tox21 Compounds

Molecular-graph models for absorption, distribution, metabolism and excretion (ADME)-related properties were compared with measured metabolic stability, solubility and passive permeability in selected Tox21 compounds.

Multiple OrgansComputational Models
t-SNE and PCA projections comparing compounds in the ADME@NCATS and Tox21 collections.
Chemical-space comparison of the ADME@NCATS and Tox21 collections. (Shah et al., Scientific Reports, 2026, Fig. 2, CC BY 4.0)

Research Applications

Early compound prioritization when cellular bioactivity data need exposure-related context. This is a laboratory ADME benchmark, not a validated in vivo toxicity predictor.

Model & Design

NCATS used ADME@NCATS molecular-graph models, including directed message-passing neural networks, to predict rat liver microsomal stability, kinetic aqueous solubility, passive gastrointestinal permeability and passive blood–brain barrier permeability.

Experimental Format

The basic ADME comparison covered 180 selected compounds; 80 existing measurements were removed from model training before evaluation and 100 compounds underwent new assays. A separate availability-selected set of 55 acetylcholinesterase-active compounds was tested for passive blood–brain barrier permeability.

Model Components

  • Component

    Tox21 compound sets

    Description

    A selected 180-compound subset for basic ADME endpoints and a separate, nonrandom 55-compound subset for the blood–brain barrier assay. The sets include drugs and other chemicals.

  • Component

    Laboratory comparators

    Description

    Rat liver microsomal stability, kinetic aqueous solubility, PAMPA gastrointestinal permeability and PAMPA blood–brain barrier permeability, each compared with its corresponding model output.

Measurements & Endpoints

  • Endpoint-specific ROC AUC, balanced accuracy, sensitivity and specificity
  • Structural similarity to training compounds and classification errors by similarity range
  • Passive permeability in PAMPA, distinct from active transport and in vivo exposure

Analysis

Compare each model with its corresponding experimental classification; remove overlapping measured compounds from training; examine structural similarity and errors across chemical space. Interpret the basic ADME and blood–brain barrier sets separately.

Published Evidence

  • Study Design

    Experimental classification of selected Tox21 compounds for three basic ADME endpoints after training-overlap removal.

    Study Sample

    180 compounds; 80 with prior NCATS measurements were removed from training and 100 were newly assayed. Endpoint measurements had some missing or inconclusive values.

    Comparison

    Laboratory rat liver microsomal stability, PAMPA gastrointestinal permeability and aqueous solubility.

    Finding

    ROC AUC: 0.87 for rat liver microsomal stability, 0.92 for PAMPA gastrointestinal permeability and 0.81 for aqueous solubility. Balanced accuracy was 0.78, 0.81 and 0.75, respectively; solubility overall accuracy was 0.83, sensitivity 0.59 and specificity 0.91. Approximately 40% of selected compounds had maximum Tanimoto similarity at or below 0.4 to relevant training data.

    Interpretation & Limits
    • The selected set is not the complete Tox21 library.
    • Prediction accuracy can vary by endpoint and represented chemical space.
    • Rat microsomal stability and artificial-membrane permeability do not measure complete human disposition.
  • Study Design

    Separate comparison of passive blood–brain barrier permeability predictions with PAMPA-BBB measurements.

    Study Sample

    55 available acetylcholinesterase-active compounds, not a random or fully representative Tox21 sample.

    Comparison

    Corresponding PAMPA-BBB passive-permeability classification.

    Finding

    ROC AUC 0.77; overall and balanced accuracy both 0.76; sensitivity 0.68 and specificity 0.85.

    Interpretation & Limits
    • PAMPA-BBB does not represent active influx or efflux transport.
    • Compound availability influenced selection.
    • The study did not establish that ADME-plus-Tox21 ranking improves prediction of in vivo toxicity or human risk.

Sources

  1. Shah P et al. Integrating in silico ADME modeling with Tox21 toxicity profiling to support compound prioritization. Scientific Reports. 2026. DOI: 10.1038/s41598-026-74876-8. ↗