lamm-mit/tdc

TDC – Binding Effect Prediction

Predict binding-related effects for small molecules using pre-trained models from Therapeutics Data Commons (TDC) on Hugging Face. Uses SMILES as input and returns classification or scores.

Overview

• Blood–brain barrier (BBB): Will the compound cross the BBB? (binary)
• hERG blockade: Cardiotoxicity risk – does it block hERG? (binary)
• CYP3A4 inhibition: Metabolism – does it inhibit CYP3A4? (binary)

Models: AttentiveFP (graph), CNN, or Morgan fingerprints. Same task, different architectures.

Prerequisites

Install TDC and DeepPurpose (optional; needed for prediction). See ScienceClaw requirements.txt or:

pip install PyTDC DeepPurpose
pip install 'dgl' 'torch'

Usage

Run with the conda environment tdc (PyTDC/DGL are installed there). Use: conda run -n tdc python ... or activate the env first.

Predict with one model (SMILES required)

conda run -n tdc python {baseDir}/scripts/tdc_predict.py --smiles "CC(=O)OC1=CC=CC=C1C(=O)O" --model BBB_Martins-AttentiveFP

Predict hERG blockade (cardiotoxicity)

conda run -n tdc python {baseDir}/scripts/tdc_predict.py --smiles "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" --model herg_karim-AttentiveFP

List available models

conda run -n tdc python {baseDir}/scripts/tdc_predict.py --list-models

Parameters

| Parameter | Description | Default | |-----------------|--------------------------------------------------|--------------------------| | --smiles | Single SMILES string | - | | --smiles-file | File with one SMILES per line | - | | --model | TDC model name (see --list-models) | BBB_Martins-AttentiveFP | | --list-models | Print available models and exit | - | | --format | Output: summary, json | summary |

Notes

• First run downloads the model from Hugging Face (cached in ~/.scienceclaw/tdc_models).
• Input must be valid SMILES; get SMILES from PubChem or ChEMBL if you have a name or ID.
• References: TDC, Hugging Face tdc.