The journey to bring a new drug from concept to market takes over 10 years, costs 2.5B dollars, and faces a failure rate of over 90%.
Target identification is the first and most crucial step in drug discovery, influencing the probability of success at every subsequent step of drug development. We have hundreds of thousands of potential targets and their combinations to choose from, but determining the right one for a specific disease remains a challenge. To address this, we tap into the primary source of data: academic researchers.
Elnora AI is revolutionizing drug discovery by harnessing the power of AI and the academic research community. This approach accelerates the R&D process, makes it faster, more cost-effective, and more reliable by avoiding repeated mistakes.
Our platform collects ML-readable, detailed data on experiment conditions and outcomes. This includes failed experiments, repeated trials under various conditions, and studies conducted worldwide. We also extract data from publicly available datasets and scientific papers, integrating it with researchers raw data.
Subsequently, we feed this comprehensive dataset into our AI models to identify the fastest, most efficient path to groundbreaking discoveries. Elnora can replicate the process of finding novel drug targets likely to succeed in subsequent phases of development. By combining public and proprietary datasets, we can predict success scores for each s in the identification of a viable drug target.
As a techbio company guided by triple bottom line principles and driven by a mission to have a real impact, we are leveraging the Elnora platform ourselves to chart a new course in antiviral drug discovery. While thousands of viruses threaten human health, the antiviral drug solutions are limited. Our unique approach focuses on host cells rather than directly targeting viruses, offering potential resistance to viral mutations and broad-spectrum efficacy against a range of viral strains.
