alterlab-pytdc

Files (8)

• SKILL.md
• evals.json · evals/
• datasets.md · references/
• oracles.md · references/
• utilities.md · references/
• benchmark_evaluation.py · scripts/
• load_and_split_data.py · scripts/
• molecular_generation.py · scripts/

SKILL.md (first lines)

# PyTDC (Therapeutics Data Commons)

## Overview

PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. Access curated datasets spanning the entire therapeutics pipeline with standardized evaluation metrics and meaningful data splits, organized into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions, DDI), and generation (molecule generation, retrosynthesis).

## When to Use This Skill

This skill should be used when:
- Working with drug discovery or therapeutic ML datasets
- Benchmarking machine learning models on standardized pharmaceutical tasks
- Predicting molecular properties (ADME, toxicity, bioactivity)
- Predicting drug-target or drug-drug interactions
- Generating novel molecules with desired properties
- Accessing curated datasets with proper train/test splits (scaffold, cold-split)
- Using molecular oracles for property optimization

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| Featurizing the loaded molecules and training/evaluating models | `alterlab-deepchem` (models) or `alterlab-molfeat` (features) |
| Pulling raw bioactivity records (IC50/Ki) for a target beyond TDC's curated sets | `alterlab-chembl` |
| Measured binding affinities for a protein target | `alterlab-bindingdb` |
| Querying the PrimeKG knowledge graph itself (drug–disease paths) | `alterlab-primekg` |

## Installation & Setup

Give PyTDC its own virtual environment:

```bash
uv venv --python 3.12        # PyTDC 1.1.15 resolves on Python 3.10-3.12, not 3.13
uv pip install PyTDC         # 1.1.15 is the latest release (Mar 2025)
```

PyTDC 1.1.15 hard-pins an older stack — `numpy<2`, `pandas<3`, `rdkit<2024.3.1`, `transformers<4.51`, `datasets<2.20` — and pulls heavy extras such as `cellxgene-census` and `tiledbsoma`, so it cannot share an environment with current RDKit 2026.x, numpy 2.x, pandas 3.x, or transformers 5.x. Export the splits to CSV/Parquet and featurize/train in a separate environment if you need newer libraries.

## Quick Start

The basic pattern for accessing any TDC dataset follows this structure:

```python
from tdc.<problem> import <Task>
data = <Task>(name='<Dataset>')
split = data.get_split(method='scaffold', seed=1, frac=[0.7, 0.1, 0.2])
df = data.get_data(format='df')
```

Where:
- `<problem>`: One of `single_pred`, `multi_pred`, or `generation`
- `<Task>`: Specific task category (e.g., ADME, DTI, MolGen)
- `<Dataset>`: Dataset name within that task

**Example - Loading ADME data:**

```python
from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold')
# Returns dict with 'train', 'valid', 'test' DataFrames
```

## Single-Instance Prediction Tasks

Single-instance prediction involves forecasting properties of individual biomedical entities (molecules, proteins, etc.).

### Available Task Categories

#### 1. ADME (Absorption, Distribution, Metabolism, Excretion)

Predict pharmacokinetic properties of drug molecules.

```python
from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')  # Intestinal permeability
# Other datasets: HIA_Hou, Bioavailability_Ma, Lipophilicity_AstraZeneca, etc.
```

**Common ADME datasets:**
- Caco2 - Intestinal permeability
- HIA - Human intestinal absorption
- Bioavailability - Oral bioavailability
- Lipophilicity - Octanol-water partition coefficient
- Solubility - Aqueous solubility
- BBB - Blood-brain barrier penetration
- CYP - Cytochrome P450 metabolism

#### 2. Toxicity (Tox)

Predict toxicity and adverse effects of compounds.

```python
from tdc.single_pred import Tox
data = Tox(name='hERG')  # Cardiotoxicity
# Other datasets: AMES, DILI, Carcinogens_Lagunin, etc.
```

**Common toxicity datasets:**
- hERG - Cardiac toxicity
- AMES - Mutagenicity
- DILI - Drug-induced liver injury
- Carcinogens - Carcinogenicity
- ClinTox - Clinical trial toxicity

#### 3. HTS (High-Throughput Screening)

Bioactivity predictions from screening data.

```python
from tdc.single_pred import HTS
data = HTS(name='SARSCoV2_Vitro_Touret')
```

#### 4. QM (Quantum Mechanics)

Quantum mechanical properties of molecules.

```python
from tdc.single_pred import QM

From AlterLab-IEU/AlterLab-Academic-Skills, MIT.

More from AlterLab-IEU/AlterLab-Academic-Skills