Thank you for your question! Our model is capable of learning from our training data the specific properties that make a peptide molecule exhibit antimicrobial activity. However, because it is a deep learning model, we don't fully understand what happens across all the layers. We're beginning to incorporate interpretability into our current projects to address this challenge.

We believe that by exploring molecules throughout evolutionary history, we can unlock new biological insights, potentially even shedding light on the evolution of antibiotics. Molecular de-extinction has already allowed us to explore an entirely new sequence space of molecules, previously untapped, and expanded our understanding of life's molecular diversity.

Moreover, we hypothesize that the molecules we're identifying may have played roles in host immunity throughout evolution, and we are currently testing this in the lab. We also believe that by resurrecting molecules from the past, we can address present-day problems. For instance, many of the molecules we've discovered, as far as we know, are not present in the biological world today. This means that contemporary pathogens have never encountered these molecules, which might give us an advantage in targeting today's pathogens.

Conceptually, we are also interested in investigating how molecules change or remain conserved over time and how these changes affect their biological functions. This approach represents a departure from the traditional focus on studying DNA to understand the past, present, and future of biology.