Datasets:
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license: mit
task_categories:
- text-generation
tags:
- chemistry
- drug-discovery
- smiles
- molecules
- instruction-tuning
- retrosynthesis
- synthesis-planning
size_categories:
- 1M<n<10M
configs:
- config_name: all
data_files: all/*.parquet
default: true
- config_name: smileyllama
data_files: smileyllama/*.parquet
- config_name: linkllama
data_files: linkllama/*.parquet
- config_name: synllama
data_files: synllama/*.parquet
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
---
# CADD-Instruct
An instruction-tuning dataset for computer-aided drug design (CADD). It combines
three complementary tasks:
- **Conditional molecule generation** (`smileyllama`, `linkllama`) — generate a
SMILES string satisfying a set of physicochemical/structural constraints.
- **Retrosynthesis / synthesis planning** (`synllama`) — given a target
molecule, produce a synthetic pathway of reaction templates and building
blocks.
## Format
Every row follows the standard instruction / input / output schema:
| field | description |
| --- | --- |
| `instruction` | The system-level role/task description. |
| `input` | The user request (target properties, or a target SMILES to synthesize). |
| `output` | The answer — a SMILES string, or a JSON synthesis pathway (`synllama`). |
| `source` | Origin of the row (`smileyllama`, `linkllama`, or `synllama`). |
### Examples
Conditional generation (`smileyllama`):
```json
{
"instruction": "You love and excel at generating SMILES strings of drug-like molecules",
"input": "Output a SMILES string for a drug-like molecule with the following properties: <= 90 TPSA, lacks bad SMARTS, has covalent warheads (acrylamides), <= 5 LogP, <= 0.4 Fraction sp3, <= 10 Rotatable bonds:",
"output": "n1cc(/C=C/C(=O)Nc2ccc(cc2)COC)c(cc1)-c1cnn(C)c1",
"source": "smileyllama"
}
```
Linker generation (`linkllama`):
```json
{
"instruction": "You are an expert medicinal chemist specializing in linker design. Your task is to design a linker to connect given fragments and deduce whether the final molecule is chemically reasonable. The output should be in JSON format.",
"input": "Fragment 1 (SMILES: c1cc2nncn2nc1[*:1]) and Fragment 2 (SMILES: c1ccc([*:2])cc1). The distance between the attachment points is 6.59 Angstroms, and the angle between them is 114.60 degrees. Given the above information about the fragments and attachment points, design a branched linker with >= 1 rotatable bonds, >= 5 heavy atoms to connect them. The final molecule should be unreasonable. And it should have the following properties: <= 5H-bond donors, <= 7 H-bond acceptors, <= 600 Molecular weight, <= 200 TPSA.",
"output": "{\"linker\": \"BrC(=C\\\\[*:2])/C=N/N[*:1]\", \"reasoning\": \"Linker bad rings: pass. Linker problematic ring: absent. Undesirable SMARTS: pass. PAINS: pass. REOS failed rule: imine.\"}",
"source": "linkllama"
}
```
Synthesis planning (`synllama`):
```json
{
"instruction": "You are an expert synthetic organic chemist. Your task is to design a synthesis pathway for a given target molecule using common and reliable reaction templates and building blocks. ...",
"input": "Provide a synthetic pathway for this SMILES string: CNC1(CC(=O)[C@]2(...))CCCCC1",
"output": "{\"reactions\": [{\"reaction_number\": 1, \"reaction_template\": \"<rxn>...</rxn>\", \"reactants\": [...]}]}",
"source": "synllama"
}
```
## Configurations
| config | rows | description |
| --- | --- | --- |
| `all` (default) | ~5.86M | Combined dataset (all sources). |
| `smileyllama` | ~2.33M | SmileyLlama SFT data (conditional generation). |
| `linkllama` | ~1.53M | LinkLlama cap50 training data (conditional generation). |
| `synllama` | 2.00M | SynLlama synthesis-pathway data (synthesis planning). |
## Usage
Load the full dataset:
```python
from datasets import load_dataset
ds = load_dataset("lukaskim/cadd-instruct") # default "all" config
print(ds["train"][0])
```
Filter the combined dataset by `source`:
```python
from datasets import load_dataset
ds = load_dataset("lukaskim/cadd-instruct", split="train")
smiley = ds.filter(lambda ex: ex["source"] == "smileyllama")
```
Or load a specific source directly:
```python
from datasets import load_dataset
smiley = load_dataset("lukaskim/cadd-instruct", "smileyllama")
link = load_dataset("lukaskim/cadd-instruct", "linkllama")
```
Stream instead of downloading everything up front:
```python
from datasets import load_dataset
ds = load_dataset("lukaskim/cadd-instruct", split="train", streaming=True)
for example in ds:
print(example["input"], "->", example["output"])
break
```
Format a row into a prompt for supervised fine-tuning:
```python
from datasets import load_dataset
ds = load_dataset("lukaskim/cadd-instruct", split="train")
def to_prompt(ex):
return {"text": f"{ex['instruction']}\n\n{ex['input']}\n{ex['output']}"}
ds = ds.map(to_prompt)
print(ds[0]["text"])
```
## Sources
- **smileyllama**: SFT data for SmileyLlama — https://figshare.com/articles/dataset/SFT_Data_for_SmileyLlama/30854573
- **linkllama**: https://huggingface.co/datasets/THGLab/LinkLlama-cap50-train
- **synllama**: https://github.com/THGLab/SynLlama/tree/main
|