Use Cases

Advancing Drug Discovery with Artificial Intelligence

Discover how to enhance your drug discovery pipeline using our novel Artificial Intelligence technology

DRUG DISCOVERY - A LENGTHY AND ESSENTIAL ACTIVITY

Drug discovery is a field demanding vast resources, is time-intensive, and requires an integration of a wide variety of specialized skills in each step of the process. In addition, it is also a particularly uncertain process, where even after many years of research and development many “promising” drugs still don’t make it to the market. It is estimated that the research and development journey of bringing a new drug to a market lasts around 12 years and costs around $1.3 billion on average.

OUR TECHNOLOGY FOR DRUG DISCOVERY

Kantify has developed an end-to-end software platform that leverages modern technologies, curated data, and a set of breakthrough algorithmic innovations to do most of the drug discovery in silico - making the whole process better, faster, cheaper, and more individualized. In particular, our technology can help:

Identify the macromolecule target that is causing the disease

One of the most important steps in developing a new drug is target identification and validation. Depending on the complexity of the disease, the targets can be in the form of proteins, genes, and RNA. Our in-silico platform employs the output of phenotypic screens to identify disease targets without a-priori knowledge or bias. Thanks to AI-based computational methods, we are able to identify relevant disease targets, enhance drug repurposing, and unlock personalized medicine and treatments.

Find novel hits from diverse chemical scaffolds

Once a disease target is known, researchers focus on finding drug candidates that meaningfully interact with the selected target. Traditional methods revolve around High Throughput Screening (HTS), which involves the use of complex and expensive laboratory automation machines in order to screen a compound library against the drug target and identify the molecules which interact with the target in a desired manner. While the traditional HTS process is still predominant in hit identification, it is limited, very costly, and time-consuming.

Using artificial intelligence computational techniques, we have developed an in silico solution for virtual High Throughput Screening (vHTS) in order to overcome the challenges of today's HTS hit finding. By doing most of the work in silico rather than in a wet lab, we are making the whole process of finding (novel) hits from diverse chemical scaffolds cheaper and faster, reducing the screening time to hours rather than months and years. By leveraging some of the latest technologies and techniques in machine learning, and thanks to a set of breakthrough innovations of our own, our algorithms largely beat the state of the art in vHTs, resulting in significantly better predictions of which compounds are likely hits. Furthermore, we work on “generalizable” or “zero shot” approaches - meaning that no prior data of a particular screen needs to exist to start making accurate predictions.

Develop promising lead compounds making sure they are both to be effective and not have adverse effects

Once drug hits with the desired activity are identified, comes the process of selecting promising leads which are characterized by both efficacy and safety of the compound. Our team of AI experts has developed a set of breakthrough computational solutions that can analyze and predict the physicochemical properties of the compounds and the likelihood of off-target hits to help researchers and scientists select the ones which are most likely to succeed in clinical trials and not cause any side effects.

Reduce clinical trial failure rate

The primary source of clinical trial failure remains the inability to demonstrate efficacy and safety in drug candidates. Bringing new drugs to market, apart from being extremely challenging, time extensive, costly, it is also a high-risk process with a significant failure rate. By leveraging genomic data, we are capable of predicting whether patients are likely to benefit from the drugs we develop, reducing both the cost and risk of failure of clinical trials.

At Kantify we constantly work on developing state-of-the-art AI technologies and computational techniques to help improve human health and support drug discovery for people suffering from countless diseases. We are trusted by pharma, biotech, and contract research organizations, academics and researchers around the globe. If you are interested in a collaboration or have a specific project in mind, leave us a message!

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