Drug development is notoriously expensive. The cost to develop a new drug can reach as high as $2.5 billion, with 90% of candidates ultimately failing in clinical trials. Yet, the potential for groundbreaking therapies has expanded thanks to advances in combinatorial chemistry, which have exponentially increased the diversity of accessible compounds.
However, with trillions of potential candidates in the chemical space, the challenge remains: how do we efficiently identify the most promising compounds for further exploration?
Breaking Barriers to Drug Discovery
Virtual screening and high-throughput assays have been critical tools in filtering chemical libraries, but as these libraries grow larger and more complex, traditional methods have struggled to keep pace. The pharmaceutical industry needs a paradigm shift—a way to sift through immense chemical spaces in a fraction of the time and cost.
Optic streamlines the process, accelerating the identification of promising small molecules and pushing them faster through the pipeline, ultimately benefiting patients sooner. By leveraging state-of-the-art machine learning (ML) techniques, Optic provides a platform for helping filter through the noise of millions of small molecule drug candidates.
The Target-Agnostic Approach to Molecule Discovery
Optic's solution is not limited by the traditional reliance on structural similarities, which often confine drug discovery efforts to well-trodden paths. Instead, the platform employs target-agnostic models capable of identifying novel candidates based on shared biological activity, even across structurally diverse chemical spaces.
Unlike legacy tools like QSAR models or protein-ligand docking, which struggle with generalization and rely on comparisons to known data, Bioptic's innovative approach focuses on extrapolating beyond the known. By training its algorithms using HI (Hit Identification) splitting methods, Optic ensures its models are tested on structurally novel molecules, fostering more accurate predictions in unexplored regions of chemical space.
Achieving High Hit Rates with Novel Techniques
Optic AI's models are rigorously validated with real-world wet-lab testing, a critical benchmark for success. Their ability to achieve hit rates of 5% to 8% at a 10-µM activity cutoff exceeds industry standards for early-stage drug discovery campaigns. This remarkable efficiency stems from a combination of using challenging datasets that stress-test models' ability to generalize across chemical spaces, focusing on shared biological activity rather than structural similarities, which expands the scope of discovery and ensures computational predictions translate into viable leads in experimental assays.
A New Era of AI-Driven Discovery
The growth of chemical libraries and the increasing complexity of drug discovery demand tools that are not just faster but also smarter. Optic is paving the way with its robust, scalable, and scientifically validated models. These advancements promise to transform how the pharmaceutical industry approaches innovation—making the discovery of life-saving drugs more efficient, cost-effective, and far-reaching.
In an industry where time is critical and precision is paramount, Optic is not just improving the process—it's redefining it.
