5 Top Artificial Intelligence Solutions Impacting Drug Discovery StartUs Insights

5 Top Artificial Intelligence Startups Impacting Drug Discovery

We analyzed 163 artificial intelligence startups impacting drug discovery. Standigm, CytoReason, Genome Biologics, BullFrog AI, and DeepCure develop 5 top solutions. Learn more in our Global Startup Heat Map!

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the pharma sector. As there are many startups working on a variety of applications, we want to share our insights with you. This time, we take a look at 5 promising artificial intelligence (AI) startups impacting drug discovery.

Heat Map: 5 Top Artificial Intelligence Startups Impacting Drug Discovery

Using our StartUs Insights Platform, covering 1.116.000+ startups & emerging companies, we looked at innovation in the drug discovery field. For this research, we identified 163 relevant solutions and picked 5 to showcase below. These companies were chosen based on a data-driven startup scouting approach, taking into account factors such as location, founding year, and technology among others. Depending on your specific criteria, the top picks might look entirely different.

The Global Startup Heat Map below highlights 5 startups & emerging companies developing Artificial Intelligence-based solutions for drug discovery. Moreover, the Heat Map reveals regions that observe a high startup activity and illustrates the geographic distribution of all 163 companies we analyzed for this specific topic.


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Which startups develop the other 158 solutions?


Standigm – Novel Drug Design

Drug development often takes a decade of research and billions in investment before a drug reaches the market. This is because the process begins with a chemical space of billions of compounds before some candidates are identified for clinical trials. Artificial Intelligence solutions allow researchers to more quickly design novel drugs that display the desired properties. Other than chemical data, they also analyze biomedical literature to speed up de novo drug design.

South Korean startup Standigm offers novel drug design solutions. Standigm BEST explores an AI-generated latent chemical space to generate novel compounds with desired properties. Once the candidates have been identified, Standigm Insights provides biological interpretations to discover pathways and therapeutics patterns and prioritize potential targets. The startup’s solutions eliminate the uncertainty in the drug discovery process to save time and costs during development.

CytoReason – Data-Driven Target Discovery

An early step in the drug discovery process involves the identification of target molecules in human bodies that the drugs are designed to interact with. However, little is known about a large number of human genes and proteins. The rapid growth of genomics, proteomics, and other omics data in the last two decades has generated large amounts of biological data. AI-based solutions explore these datasets to offer insights on prospective targets for any disease.

Israel-based startup CytoReason analyzes multi-omic, human clinical data to offer data-driven target discovery. The startup’s platform uses vast amounts of proprietary and public data to understand the complex systems of interactions inside cells. The solution uses machine learning algorithms and continual statistical learning to uncover disease-related cell/gene maps. The platform supports research and development (R&D) efforts across the drug development cycle.

Genome Biologics – Preclinical Drug Discovery

Preclinical testing involves testing drugs for toxicity, pharmacodynamics, pharmacokinetics, etc. before clinical trials. AI-based solutions minimize the uncertainty in preclinical experiments by automating the analysis of samples for the effects of the drugs. The scalability offered by Artificial Intelligence allows testing novel and existing drugs for a large number of targets at once.

Genome Biologics is a German startup that develops solutions for preclinical drug discovery. GENIMPAS® uses pattern recognition and machine learning to match compound databases and drug discovery and repositioning pipelines with profiles of disease-relevant genes. This allows the startup to identify novel compounds and repurpose known compounds to treat cardiometabolic and cardiovascular diseases and cancers. GENISYST® is a patented technology for multiplexed diseased modeling that uses single-cell transgenics for preclinical testing.

BullFrog AI – Late-Stug Drug Candidates

Just like the other steps in drug discovery, the success rate of clinical trials is very low. To support this rate rise, Natural language processing (NLP) methods scan medical and pathology reports to identify suitable patients for clinical trials. Other AI techniques help researchers and clinicians to gather better insights about the efficacy of clinical trials and recommend appropriate changes or novel use cases.

The US-based startup BullFrog AI develops a proprietary AI platform to enable precision medicine. bfLEAPTM analyzes clinical trial data sets to identify relationships and correlations between therapies and patients to discover novel insights for late-stage drug candidates. The platform parses through complex data to discover novel drug targets, find niche patient populations that may benefit greatly from a drug, and identify synergistic combinations of drugs.

DeepCure – Small Molecule Therapeutics

Small molecules comprise a large portion of drugs in the market. They are often easy to develop from derivatives of known therapeutic compounds. However, they may participate in other interactions that minimize their safety or efficacy. Machine learning algorithms learn from publically available structural and chemical data of small molecules to identify therapeutics with desired properties.

DeepCure is a US-based startup that uses deep learning to discover small molecule therapeutics. The startup combine AI algorithms, cloud computing, and MolDBTM, their proprietary database with over a trillion unique molecules. The solution identifies the most promising small molecules to reduce time and cost in later development stages. It accounts for the dynamic nature of the target and optimizes for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.

What About The Other 158 Solutions?

While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence so you can achieve your goals faster. The 5 artificial intelligence startups showcased above are promising examples out of 163 we analyzed for this article. To identify the most relevant solutions based on your specific criteria, get in touch.

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