In the rapidly evolving landscape of artificial intelligence, the domain of molecular generation is a fascinating frontier. Today, we dive into MolGen-large—an innovative model designed to generate chemically valid molecules using a unique representation known as SELFIES. This blog will guide you through its functionalities and use, ensuring you can harness its full potential.

What is MolGen-large?

MolGen-large is a pre-trained molecular generative model introduced in the paper Domain-Agnostic Molecular Generation with Self-feedback. It is the first of its kind that exclusively produces chemically valid molecules. With a training dataset consisting of over 100 million molecules represented in SELFIES, MolGen-large captures intrinsic structural patterns by mapping corrupted SELFIES to their original forms.

To achieve this, it utilizes a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder, along with a sophisticated multi-task molecular prefix tuning (MPT) to yield molecules with desired properties. Essentially, think of it like an artist who learns from millions of paintings to create new and unique masterpieces, ensuring each piece adheres to the norms of art—just as MolGen-large adheres to the rules of chemistry.

Intended Uses of MolGen-large

• Molecule generation: Use the raw model for generating new molecules.
• Fine-tuning: Adapt the model to specific downstream tasks based on your requirements.

For comprehensive details about fine-tuning for specific objectives, explore the repository.

How to Generate Molecules with MolGen-large

To utilize the MolGen-large model for molecule generation, follow these steps in Python:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large")

sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")

# beam search
molecules = model.generate(input_ids=sf_input["input_ids"],
                            attention_mask=sf_input["attention_mask"],
                            max_length=15,
                            min_length=5,
                            num_return_sequences=5,
                            num_beams=5)

sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]

In this code, you first import the necessary libraries. You then specify the molecule input in SELFIES format, and the model is instructed to generate sequences based on your input. The `sf_output` variable will contain the generated molecules, which will be represented in the SELFIES format.

Troubleshooting Your Molecule Generation

While using MolGen-large, you might encounter some hiccups. Here are a few troubleshooting tips to consider:

• If your model is not generating the expected output, check if the input format adheres to SELFIES representation.
• Ensure that you installed the necessary libraries and dependencies for the transformers toolkit.
• If the model seems slow, experiment with altering parameters like `num_return_sequences` or `num_beams` for optimal performance.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

MolGen-large is a powerful tool for anyone venturing into the world of molecular generation, providing a bridge between complex data and usable structures. With the ability to generate chemically valid molecules and the flexibility for fine-tuning, it’s a game-changer in molecular optimization. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.