openadmet/Octant_CYP_inhibition_reactivity_blog_release

OpenADMET Octant CYP Inhibition & Reactivity

Data release from the OpenADMET consortium, generated by Octant Bio. This dataset accompanies the blog post **Building the OpenADMET Data Engine**. Source code, assay protocols, and raw TSV files are on GitHub.

Overview

Cytochrome P450 (CYP) enzymes drive the oxidative metabolism of most drugs and are a primary cause of drug-drug interactions (DDIs). Despite their importance, public CYP datasets are sparse, noisy, and collected under inconsistent conditions — making them unreliable for machine learning.

This release provides self-consistent, multi-endpoint CYP data generated on a single platform under controlled conditions, with full well-level readouts and quality annotations. Approximately 1,200 compounds from a diversity chemical library were screened for:

• —CYP3A4 and CYP2J2 reaction phenotyping (substrate identification via Echo acoustic ejection mass spectrometry, 2 µL, 1536-well format)
• —CYP3A4 inhibition (fluorescence-based dose-response curves, 4 µL, 1536-well format, with a 30-minute active-enzyme pre-incubation to capture both reversible and time-dependent inhibitors)

The dataset is structured as two tiers — compound-level summaries for modeling, and well-level detail for QC, outlier analysis, and advanced modeling.

Subsets

inhibition

1,340 rows — one row per compound

Compound-level CYP3A4 inhibition summary derived from 12-point dose-response curves. Use this for ML model training on inhibition potency.

Why collected: Provides quantitative inhibition potency for the full library. The active-enzyme pre-incubation means IC₅₀ values reflect combined reversible + time-dependent inhibition — important for DDI risk assessment but distinct from standard reversible-only IC₅₀ measurements.

inhibition_detailed

16,931 rows — well-level fluorescence from dose-response assays

Raw fluorescence readouts underlying the inhibition summaries. Use this for QC analysis, outlier investigation, and training models on raw assay signals.

Why collected: Summary IC₅₀ values hide plate artifacts, outlier wells, and edge effects. Well-level data allows modelers to apply their own QC criteria, detect spatial plate effects, and train on richer experimental signals.

reactivity

2,442 rows — one row per compound per enzyme

Compound-level CYP reactivity summary (substrate depletion). One row per compound-enzyme pair (CYP3A4 and CYP2J2). Use this for ML model training on metabolic substrate status.

Why collected: Reaction phenotyping identifies whether a compound is a CYP substrate, a prerequisite for understanding metabolic clearance and DDI. CYP2J2 was prioritized alongside CYP3A4 because of its role in extra-hepatic metabolism and its relative absence in public datasets.

reactivity_detailed

19,344 rows — well-level Echo-MS peak areas

Raw acoustic ejection mass spectrometry (Echo-MS) peak areas underlying the reactivity summaries. Use this for QC analysis, understanding measurement variability, and training on raw MS signals.

Why collected: Echo-MS enables sub-2-second contactless sampling from 1536-well plates. Well-level peak areas with 4 biological replicates per condition provide statistical power for depletion detection and allow downstream reproducibility and noise modeling.

will_it_fly

11,353 rows — ionization buffer comparison

Echo-MS peak areas for ~11,000 library compounds measured under two carrier solvent conditions (ammonium formate vs. ammonium fluoride), without enzyme. Used to pre-profile chemical libraries for MS compatibility before reactivity assays.

Why collected: Not every molecule ionizes well under generic untargeted TOF-MS conditions. Pre-profiling identifies compounds that cannot be reliably detected ("won't fly") before they enter the assay, preventing false negatives. This data also quantifies how switching from ammonium formate to ammonium fluoride expanded chemical coverage from ~50% to ~75% of the library — directly informing assay design decisions.

Loading the Data

from datasets import load_dataset

# Compound-level summaries (for ML)
inhibition   = load_dataset("openadmet/Octant_CYP_inhibition_reactivity_blog_release", "inhibition")
reactivity   = load_dataset("openadmet/Octant_CYP_inhibition_reactivity_blog_release", "reactivity")

# Well-level detail (for QC / advanced modeling)
inhib_wells  = load_dataset("openadmet/Octant_CYP_inhibition_reactivity_blog_release", "inhibition_detailed")
react_wells  = load_dataset("openadmet/Octant_CYP_inhibition_reactivity_blog_release", "reactivity_detailed")

# Ionization profiling
will_it_fly  = load_dataset("openadmet/Octant_CYP_inhibition_reactivity_blog_release", "will_it_fly")

Key Design Choices

• —Active-enzyme pre-incubation in the inhibition assay: IC₅₀ values reflect reversible inhibition plus any time-dependent effects that develop during the 30-minute pre-incubation. This differs from standard reversible-only IC₅₀ assays.
• —1536-well miniaturization: 4 µL (inhibition) and 2 µL (reactivity) assay volumes reduce cost ~100× vs. standard CRO formats while maintaining biological relevance using industry-standard Gentest Supersomes.
• —Echo-MS (acoustic ejection MS): Enables label-free, contactless sub-2-second sampling. Only compounds that ionize above background are reported — non-detecting compounds are excluded rather than reported as zero.
• —Full QC transparency: Well-level data, plate maps, outlier flags, and fitted curve parameters are included so modelers can apply their own quality thresholds.

Related Resources

• —Blog post: Building the OpenADMET Data Engine
• —GitHub (source code, protocols, TSV files): OpenADMET/Octant_CYP_blog_post
• —OpenADMET Discord: Join the conversation
• —Contact: openadmet@omsf.io

Citation

If you use this dataset, please cite the accompanying blog post and link to this repository.

License: Apache 2.0