DT Approach - Denovicon Therapeutics

Platform & Expertise

At Denovicon Therapeutics, the overall drug discovery process begins and ends with our revolutionary AI–physics-based platform. This means everything from the strategic identification of viable biological targets to the design of small-molecule clinical candidates is achieved with the computational platform. This along with our combined decades’ worth of drug discovery expertise (resulting in several clinical candidates) gives us the edge in modern-day drug discovery.

target selection

From the very start, our biologists and computational chemists work closely together to identify therapeutically-relevant biological pathways and targets viable for small-molecule drugs. In this way, undruggable targets are avoided and those with the highest chance of therapeutic success become the focus.

Leads, not just hits

“We’re not just finding hits, we’re finding high-quality leads that are quickly turned into clinical candidates, in a fraction of the time of traditional drug discovery.” – Scott Bembenek, CEO and Founder, Denovicon Therapeutics

Traditional drug discovery often starts with a high-throughput screen (HTS) of an in-house compound collection containing millions of compounds. The overall process takes several months and usually results in very low hit rates and low-quality chemical matter. Our strategy involves screening a virtual library of over 100 billion compounds. This results in quicker turnaround times, lower costs, and higher success rates (orders of magnitude higher than traditional HTS). Further, the resulting leads – not simply hits – provide high-quality chemical matter starting points for further optimization. In our kickoff project on next-generation PARP1 inhibitors, the platform delivered an unprecedented 80% success rate (using 50% inhibition @ 1 µM). This was followed by another impressive success rate of 59% (same stringent criteria) for their first-in-class PARP7 project.

Lead Optimization

A key aspect of our platform involves a sophisticated design strategy that explores a chemical space of billions upon billions of (~10²⁷) molecules, which strategically transforms an initial lead into a strong lead series with novel patent position (IP) and drug properties.

Clinical candidates

When optimizing a lead molecule, rather than focusing on a single property at a time, the multiple properties that are needed to move it towards drug candidacy are simultaneously addressed. This is done using molecular modeling and AI calculations along with experimental data. And as more data is generated, the calculations are iteratively refined and the models improved. This “iterative multiparameter optimization” scheme is a very unique aspect of our drug discovery platform.

Accelerated Success

While traditional drug discovery spends 5–7 years in pre-clinical discovery synthesizing 5,000–10,000 compounds attempting to find one clinical candidate, our platform lowers this timeline to 0.5–2 years with only ~50 compounds synthesized to deliver a clinical candidate. Once in clinic a much higher chance of success is expected, upwards of 40-50%, compared to the current success rate of only 13.8%.