README.md

metadata

title: BioInteract Drug-Target Interaction
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.20.0
python_version: '3.11'
app_file: app.py
pinned: false
license: mit
short_description: Binary high-affinity DTI with model-native attribution

BioInteract

BioInteract supports binary high-affinity interaction classification on the Davis kinase benchmark. The browser interface returns a classifier score and model-native atom-residue attention attribution. Attribution is hypothesis-generating; it is not physical contacts, binding-residue evidence, or a structural mechanism.

Browser demonstration scope

The custom workflow accepts a drug SMILES string and a protein sequence. It uses Hugging Face Transformers with a 512-residue input limit and initialises ESM-2 during application startup. For arbitrary user-supplied sequences, the configured domain-label channel uses an unknown-domain representation because curated annotations are not released with this demonstration.

The browser demonstration is not numerically equivalent to the reported 1,200-residue evaluation pipeline, which uses cached fair-ESM embeddings. It must not be used to reproduce the reported metrics or to interpret its sigmoid classifier score as a calibrated measure.

Reported Davis results

Partition	AUROC	AUPRC
Random	0.904	0.560
Drug-ID-held-out	0.733	0.167
Target-ID-held-out	0.930	0.525

Target-ID-held-out is an archived identifier-based split result. Duplicate Davis protein sequences mean it is not a strict exact-sequence-held-out estimate.

Interpretation limit

The heatmap and residue chart rank model-native attribution. They do not identify binding residues, key contacts, a binding pocket, or a mechanistic interaction map.