# ***Mol-MoE***: Training Preference-Guided Routers for Molecule Generation *Diego Calanzone (1, 2), Pierluca D'Oro (2), Pierre-Luc Bacon (1, 2)*
*(1) Universite de Montreal, (2) Mila Quebec AI Institute*
**arXiv**: https://arxiv.org/abs/2502.05633 **Abstract**: Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability. ## How to use this model This LM is fine-tuned to generate molecules in the SMILES format wrt. desired properties. For unconditioned SMILES generation, use the BOS token ``.
For conditioned generation, you can target the following properties: `JNK3, DRD2, GSK3B, CYP2D6, CYP2C19`. ``` prompt: ``` An example of the generation pipeline: ``` from transformers import AutoTokenizer, AutoModelForCausalLM import re # Setup device = "cuda" tokenizer = AutoTokenizer.from_pretrained("ddidacus/RiC-mol-llama-1b") model = AutoModelForCausalLM.from_pretrained("ddidacus/RiC-mol-llama-1b") generation_kwargs = { "max_new_tokens": 128, "min_length": -1, "top_k": 0.0, "top_p": 0.9, "do_sample": True, "pad_token_id": tokenizer.eos_token_id, "temperature": 1.0 } # Inference query = "" toks = tokenizer([query], return_tensors="pt")["input_ids"].to(device) output = model.generate(toks, **generation_kwargs) output = tokenizer.batch_decode(output) # Parsing filter = r'~~(.*?)~~' molecule = re.findall(filter, output[0], re.DOTALL) ``` ### Model Description This model is a fine-tuned version of LLaMa 3.2 1B through two stages: 1. Fine-tuning on ~3.5M molecules extracted from: ZINC 250K, MOSES, CHEMBL 2. RLHF-tuning using instruction fine-tuning on 5 distinct reward signals. The detailed pipeline we followed is reported in the original paper:
*"Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment" Yang et al. 2024* [1] - **Developed by:** Diego Calanzone (diego.calanzone@mila.quebec) - **Model type:** Decoder Only Transformer - **Finetuned from model [optional]:** LLaMA 3.2 1B Read the paper for further details. ### Sources [1] https://arxiv.org/abs/2402.10207

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