Gradient’s unique algorithms bridge the gap between quantum theory and molecular modeling practice, and allow the application of quantum methods to large‐scale in silico discovery of compounds with therapeutic and other properties of interest.
The technology has been applied for drug discovery and design, drug repositioning, small-molecule diagnostics, and biomarker discovery.
New chemical entity (NCE) discovery and design
Gradient’s technology is ideally suited for developing drugs against elusive, “undruggable” targets such as master regulators - transcription factors and other key proteins that influence a vast range of human diseases. More importantly, Gradient can identify and design new molecules against these elusive targets much faster and for a fraction of the cost of comparable industry drug discovery efforts (see Figure). Gradient has taken a systematic, data-driven approach to both NCE development and drug repositioning, and has developed a portfolio of quantum models for over 1,200 publicly available drug-target datasets.
In addition to NCE discovery and design for therapeutic purposes, Gradient’s technology can be applied for the discovery of small molecules to be used in molecular diagnostics. In the same way that new compounds can be designed to bind to a particular target with high affinity and specificity, Gradient’s platform can discover new molecules that could replace a primary antibody in a diagnostics assay. A diagnostic test based on small molecules is simpler, less expensive, easier to apply in a wider range of conditions, and more efficient in point-of-care (POC) settings than current antibody-based tests. Moreover, the small molecule-based platform can be extended for multiplexed and multi-analytes readouts, as well as for POC diagnosis of numerous diseases of interest.
Systematic drug repositioning
The advantages of drug repositioning are clear – a compound with known safety profile and detected novel activity can be placed directly into advanced clinical trials due to a special regulatory path (505(b)(2) - New Drug Application) for approval of improvements in existing drugs. The combination of Gradient’s extensive model portfolio and pre-computed libraries of compounds with known safety profiles produces a search-space matrix with more than 100,000,000 entries, many orders of magnitude larger than the operating space of existing drug-repositioning efforts, with critical advantages in terms of both cost and time (see Figure).