Use Cases

Drug Discovery

Use AI to identify new drug targets and biomarkers

The traditional drug discovery process is a high-risk, high-return approach that is costly and time-consuming.
For that reason, Artificial Intelligence has been attracting attention in this field as an innovative technology that can increase the possibility of success, while dramatically reducing costs and time for research and development.

Why AI is spreading to drug discovery

New drug discovery is a field that requires vast resources, is time-intensive, and requires an integration of a wide variety of specialized skills in each step of the process. It is estimated that 1 in 10 small molecules projects become candidates for clinical trials, and only about 1 in 10 of those compounds will then pass successfully through clinical trials. AI has the capacity to transform the drug development process, improving all steps during drug discovery, and making the process more efficient and more effective. Thus, benefiting all parties involved - both companies working on developing new drugs, and patients in need of viable treatments.

Our experience in drug discovery

Our journey in the biomedical sector started by developing an AI solution (in collaboration with Universite Libre de Bruxelles) that can predict an episode of Atrial Fibrillation before it occurs. We achieved a world-first in predicting this pathology. We then realized that some of our expertise and work could be used in the field of drug discovery.

We developed our own state-of-the-art solution for drug discovery. This early-stage technology is highly effective for virtual high-throughput screening (HTS). The technology has provided performances superior to common virtual screening tools, thanks to its ability to efficiently use structural data and features of compounds.

Business benefits

Discovering new and successful drugs is a hard and time-consuming process. Our virtual HTS solution can improve drug development by: * identifying more promising drug candidates * raising the “hit rate” or the percentage of drug candidates that make it through clinical trials and gain regulatory approval * speeding up the overall process *removing the high cost of high-throughput screening.

Our virtual HTS solution can help companies make their search for new pharmaceuticals quicker, cheaper, and more effective.

Increase speed

AI can save 40%-50% of the time in compound creation and screening, saving up to $26 million in screening costs per year. Our solution has the capacity to process and analyze large datasets in a fraction of time, hence increasing the speed and efficiency of the drug development process, and decreasing the time to market.

Reduce costs

The average cost to bring a new drug to the market is estimated at $2.6 billion. Our solution enable biopharma organizations to dedicate their resources on the most promising compounds and save considerable resources.

Expand capabilities

Our solution enable biopharma organizations to increase their chances to find new promising compounds, or generate new insights on existing compounds.

Let's get in touch !

AI is transforming the future of the biomedical sector, with new use cases and researches popping up on a daily basis. Leave us your contact details or give us a call and we will propose some slots to have an initial exchange.