In this edition, we’re giving you a peek under the hood at Necessary Ventures and our biotech focus. We’re backing platform companies that combine computational power with scalable wet-lab systems to speed up and enhance drug discovery. Read on to see why (full thesis on our website here).

Our Thesis

Written by Ashley Lin. 

Ashley is a MBA Candidate at the University Chicago Booth School of Business and received her B.A. in Biological Sciences and Chemistry from Cornell University. Currently, she is an intern with ARCH Venture Partners, where she supports portfolio companies on business development and analyzes potential investment opportunities. She started her career in Structural Biology and infectious diseases research, and has held roles at Moderna, Vir Biotechnology, and Academia Sinica (national lab of Taiwan). Ashley is passionate about leveraging the intersections of AI and science to drive improved patient outcomes.

What we're looking for in biotech and techbio?

At Necessary, we’re excited about platform biotech companies. The ones we’ve been most excited by generally have the following attributes:

• Robust computational platforms: i.e., screening large protein/chemical libraries at higher throughput than current methods, predicting ligand-receptor binding, using AI/ML with proprietary data to discover and/or design novel targets, small molecules, and/or proteins. We’re not disinterested in gene and cell therapies, but they’re much lower priority.

• Scaled wet lab systems that have demonstrated increased efficiency over competitors: a preferred example is using lab automation to enable 100X the assays in 1/10th the time, especially assays to provide feedback to computational hits. Other examples include efficient protein production systems and low-cost chemical synthesis processes that unlock scaled exploration and validation.

• Strategic indication selection: we like multi-indication assets and indications with significant unmet need. For indications with significant external therapeutic development, we must be convinced of abilities to surpass others and retain market share.

• Diverse partnership intent opportunities: teams that generate enough validated hits can and should pursue early partnership or licensing deals to create near-term revenue that can support the development of independent programs vs. relying solely on investors.

• Balanced founding team: a founding team that has the required skills and experience on both the science/drug discovery side as well as the business development/dealmaking side.

• Quality informative data: we expect data sets to provide proof of concept, whether it be wet lab data only or, in certain instances, additional computationally generated data.

You’ll notice that our areas of interest aren’t being categorized by indications, targets, or types of therapies. While we have opinions about those, our goal here is to emphasize what appears to be a more universal approach with regard to discovery and development that is becoming the industry standard.

A specific subset of the above that we’re excited about are AI/ML technologies that advance the protein discovery flywheel:

• Protein synthesis

  • Digital twins of the protein production process to optimize conditions and scale up efficiently

  • More accurate and high-throughput simulations to predict protein folding and help guide the design of proteins that fold more accurately

  • Computational methods to more accurately predict protein developability

• Protein sequencing

  • In-vivo protein sequencing for real-time monitoring of protein synthesis and degradation

  • Predicting protein-protein interactions and sequencing small molecules

• Mining the dark proteome & ligand discovery for intrinsically disordered proteins (IDPs)

Other areas of interest include:

• AI/ML technologies for predicting toxicity studies, in vivo data, and clinical trial probability of success (POS)

• Novel enzyme discovery

• GPCR discovery and GPCR drugs for non-sensory indications

• Selectively turning down the immune system or turning immune cells on/off

• Selectively turning proteins/DNA/RNA on/off

• Lowering in vivo and in vitro study costs

• Alternative expression systems using synthetic biology, alternative hosts, or cell-free synthesis to minimize costs and production times for difficult-to-express proteins

• Large-scale, low emissions continuous biomanufacturing at a significant scale (i.e., able to have 50+ liters/reactor)

Our thoughts on AI in drug discovery

Software and hardware are both dramatically altering the drug discovery landscape. Love it or hate it, TechBio is a real thing and heralds a new age for the industry, e.g., using advanced computational approaches such as AI/ML, big data analytics, and robotics to drive drug discovery and development. Our team has been investing here since before the term was coined, including Recursion Pharmaceuticals (NASDAQ: RXRX), a pioneer of this field that epitomizes much of this thesis. We remain big believers in the power of computation to accelerate the drug discovery process with some nuance/caveats.