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README.md

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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

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