Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- chemistry
+- molecular-generation
+- structure-based-design
+- transformer
+- reinforcement-learning
+- gpt-2
+datasets:
+- HUBioDataLab/DrugGEN-chembl-smiles
+- antoinebcx/smiles-molecules-moses
+base_model:
+- openai-community/gpt2
+pipeline_tag: reinforcement-learning
+library_name: transformers
+---
+
+# 🏂 ProteinSkier
+
+**ProteinSkier** is a GPT-2–based language model that “carves fresh lines” through chemical space, producing drug-like SMILES strings with an explicit bias toward **ADMET quality, novelty, and synthesizability**.
+
+## 1 · Why another generative model?
+
+Traditional generative models often rediscover known scaffolds or output molecules that fail late-stage ADMET filters.  
+ProteinSkier addresses this by coupling large-scale pre-training on ~2 M curated molecules **with a second-stage Reinforcement Fine-Tuning (RFT)** that rewards:
+
+| Component | Reward signal (λ) | Source |
+|-----------|-------------------|--------|
+| **Validity** | hard filter | RDKit sanitisation |
+| **QED ↑** | 0.35 | RDKit |
+| **Novelty ↑** | 0.25 | training-set hash table |
+| **Lipinski pass ↑** | 0.20 | RDKit |
+| **logP in [–1, 4]** | 0.10 | RDKit |
+| **Predicted tox ↓** | 0.10 | internal classifier |
+
+The policy is updated with *policy-gradient REINFORCE*; low-quality trajectories are rejected via an adaptive threshold (see `FullDatasetRFTTrainer` in the code).
+
+## 2 · Intended uses & scope
+
+| Stage | Example use-case | Not a good fit |
+|-------|------------------|----------------|
+| *Hit finding* | Rapidly scaffold-hop around a weak binder identified by docking. | Predicting absolute IC₅₀/Kᵢ values. |
+| *Lead optimisation* | Generating analogues that respect Lipinski & BBB guidelines. | Ensuring synthetic accessibility without chemist review. |
+| *Ideation / teaching* | Demonstrating language-model chemistry in the classroom. | Production-scale enumeration without downstream filtering. |
+
+## 3 · Quick start
+
+> Requires `transformers ≥ 4.42`, `torch ≥ 2.2`, `rdkit`, `accelerate`.
+
+```python
+from transformers import AutoTokenizer, GPT2LMHeadModel
+
+model_id = "your-org/ProteinSkier"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = GPT2LMHeadModel.from_pretrained(model_id)
+
+# Generate 5 novel molecules
+prompt = tok("<bos>", return_tensors="pt").input_ids
+gen = model.generate(
+    prompt.repeat(5, 1),
+    max_length=128,
+    do_sample=True,
+    top_p=0.95,
+    temperature=0.7,
+)
+smiles = tok.batch_decode(gen, skip_special_tokens=True)
+print("\n".join(smiles))
+```
+## 4 · Limitations & caveats
+- No guaranteed synthesizability – always perform retrosynthetic analysis.
+- Property estimators used in RFT are fast; wet-lab assays will vary.
+- Output may include patented molecules – run IP checks.
+- ADMET focus biases chemistry toward oral drugs; unsuitable for agrochemicals or materials.