Using Computers to Regrow HAIR

Over the past month, the HairDAO community has dug into computational biology and how it might help us develop new hair loss treatments. Our search for knowledge led us to explore partnerships with LabDAO, Charles River Labs, Acellera, and Insilico Medicine. We were thoroughly impressed with the companies building in the computational biology space and appreciated that each company had their own angle, whether it was a proprietary model, democratized access to compute, or in-house translational capabilities.

At a high-level, traditional computational biology can be broken into three sections:

1.) Target Identification or “Which gene/protein should we try to up/downregulate?”

2.) Protein Folding Prediction or “How will the protein produced by our gene be shaped?”

3.) Drug Selection or “Which drugs can bind to our targets and have the desired effect?”

Target Identification:

Regarding target identification, Insilico Medicine has developed PandaOmics, an AI model capable of crawling the internet to scan relevant research papers and OMICs data to suggest potential gene targets based on your indication of interest. PandaOmics then ranks gene targets based on variables like target novelty, biological relevance, confidence, commercial tractability, and safety. Interestingly, as Niklas Rindtorff pointed out on Hairy Matters 4, a dedicated community of patient-researchers might actually be better suited for the target identification step in computational biology. Not only are humans less prone to the hallucinations which often plague present-day large language models, but patient communities are able to create proprietary datasets (from their own biological data), which can be used to better inform target identification and drug discovery. Sorry computers, but you don’t have genomes yet!

As an example, after Jumpman suggested looking into Estrogen Receptor β (ERβ) agonists, Averbs123 suggested testing WAY-2000070, a known ERβ agonist. However, Jumpman had data from a group of biohackers who had already tried WAY-2000070, “They got hives but no hair”. By proceeding in that manner, the HairDAO community has generated 43 promising gene targets. Even better, our patient community does not prioritize short-term profit maximization with variables like commercial tractability but rather to solve hair loss, which we believe is the most value-accretive strategy long-term.

Protein Prediction:

Once satisfied with our gene targets, we can either use a previously observed protein structure or tools like AlphaFold and ColabFold to predict protein shape. While AlphaFold requires loads of hardware to run the required compute, ColabFold allows anybody to run its software via cloud compute. Previously, only Universities and Biotech companies (like Charles River Labs and Insilico Medicine) had access to the