Dimension has co-led a $100M follow-on investment into Monte Rosa Therapeutics (GLUE). The company is poised to realize near, middle, and long-term clinical catalysts in large, well-scoped therapeutic areas like oncology, inflammation, and immunology. Powering this translation is a discovery engine on the precipice of inflection across fidelity and scale—largely driven by advances in geometric deep learning, high-throughput experimental biology, and next-generation proteomics. We’re eager to support Markus, John, Sharon, Pablo, Magnus and the totality of Monte Rosa’s team as they navigate clinical decision making, maintain machine learning primacy, and continue amalgamating world-class talent spanning computation, molecular sciences, and clinical translation. 

When new ways to treat disease begin to find clinical success, it’s a call for celebration. Optimism is inevitable when approaches like gene editing and antibody-drug conjugates (ADCs) provide new hope for those afflicted by illness. Following decades of research, targeted protein degraders (TPDs) are finally hitting their stride. 

Many small molecule drugs bind to and temporarily inhibit proteins involved in disease. TPDs completely destroy target proteins by routing them to the proteasome—the body’s protein disposal system. This differentiated mechanism means TPDs can be effective at smaller, less frequent doses and attack proteins elusive to standard inhibitors. Excitingly, these concepts are increasingly bearing out in human trials

Proteolysis targeting chimeras (PROTACs) and molecular glue degraders (MGDs) are the two dominant TPD formats. Our research suggests MGDs can more readily benefit from existing medicinal chemistry optimization techniques, are simpler to scalably manufacture, have superior drug-like properties, and extend to targets historically inaccessible to PROTACs such as proteins lacking bindable surface pockets. 

Unfortunately, MGDs have proven difficult to develop. Most have been discovered by accident. Compared to PROTACs, MGDs sometimes struggle with selectivity, meaning they can degrade proteins other than their intended target. 

Founded in 2018, Monte Rosa has evolved a compelling MGD discovery engine—one that combines principles of rational design with fit-for-purpose technology to (a) overcome selectivity obstacles, (b) advance beyond the known alphabet of intra-target degradation motifs (i.e., degrons) to “non-canonical” degrons, and (c) expand the repertoire of usable E3 ligases—key proteins involved in a cell’s degradation machinery. Critically, this engine has quickly advanced multiple therapeutic assets in a capital efficient manner.  

The core of Monte Rosa’s discovery process revolves around protein surfaces. Like a surveyor maps the contours of the Earth, Monte Rosa’s computational platform scans the exteriors of target proteins and E3 ligases. As pioneers in applied geometric deep learning, Monte Rosa encodes the 3D geometry, flexibility, and physicochemical properties of each patch of a protein’s surface. 

By focusing on surfaces, Monte Rosa can find non-obvious complementarity between the degradation machinery and degrons on target proteins. The company combines surface-aware MGD generation algorithms and high-throughput screening (HTS) capabilities to discover molecules that can stabilize complementary protein-ligase interactions. 

In December 2023, our team attended the Conference on Neural Information Processing Systems (NeurIPS), a preeminent gathering at the interface of machine learning (ML) and the life sciences. We reported on the state-of-the-art for generative protein design, including work by Pablo Gainza, Monte Rosa’s Associate Director of Computational Sciences and AI. Noting the direction of the field, we got busy over the holidays building relationships with the management team as well as assessing key opportunities and risks of the platform and pipeline. 

Monte Rosa’s digital capabilities are supported by a robust experimental platform charged with generating ML-ready proteomics data. Following visits to both Boston and Basel, we were impressed by the company’s intentional construction of an automated suite of assays designed to systematically assess each step of MGD-mediated protein degradation. Does the MGD candidate induce proximity between the target protein and a ligase? Does the candidate form a stable three-body (ternary) complex with both proteins? Does the orientation of the ternary complex facilitate target protein degradation? Does the candidate deeply and selectively degrade the target relative to other proteins?

Using these wet and dry-lab technologies, Monte Rosa has advanced an initial wave of assets that should step-wise validate the platform’s ability to rationally design selective MGDs in the near term, extend to non-canonical degrons and novel ligases in the medium term, and explore adjacent modalities similarly undergoing translational success such as degrader-antibody conjugates (DACs) in the long term.  

Our team is looking forward to supporting Monte Rosa as they continue leading the vanguard of protein degradation. This investment marks Dimension’s first into a clinical stage company and highlights our flexibility to engage opportunities spanning the spectrum from new company incubation to growth and public markets. Likewise, our team’s experience spans company formation to public equity. It’s still early innings for protein degradation and we’re ready to get to work to push the frontier alongside Monte Rosa. 

Zavain Dar
Founder & Managing Partner
Zavain invests at the intersection and union of cutting-edge biotech and software. Zavain was previously a General Partner at Lux Capital where he led investments in companies including Recursion (NASDAQ: RXRX), which uses automation and deep learning to develop novel therapeutics.
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