---
library_name: transformers
tags: []
---
# ***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