Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the pharma sector. As there is a large number of startups working on a wide variety of solutions, we want to share our insights with you. This time, we are taking a look at 5 promising machine learning startups.
Heat Map: 5 Top Machine Learning Startups
Using our StartUs Insights Platform, covering 1.116.000+ startups & emerging companies, we looked at innovation in the field of pharmaceuticals. For this research, we identified 305 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 machine learning-based solutions for the pharma industry. Moreover, the Heat Map reveals regions that observe a high startup activity and illustrates the geographic distribution of all 305 companies we analyzed for this specific topic.
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Insitro – Machine Learning For Drug Development
The time it takes to identify, design, and test new drugs slows down the pace of innovations in the pharma sector. Powered by machine learning and deep learning algorithms, biological data such as genetic markers help algorithms predict new kinds of drugs or combinations of drugs for specific treatments. Startups and emerging companies develop ML-based solutions for efficient and low-cost drug discovery.
The US-based startup Insitro builds predictive models for drug discovery and development. The startup generates insights from genetics and phenotypes, integrates it with clinical data and ML algorithms, to understand the causal biology of diseases. In addition, the startup helps combine patient-derived, induced pluripotent stem cells (iPSCs) with ML to build in-vitro models of diseases.
InnVentis – Drug Discovery For Inflammatory Diseases
Chronic inflammatory diseases like asthma, coeliac disease, autoimmune diseases, and hepatitis make peoples’ everyday lives difficult. Clinical trial data that is run through Artificial Intelligence (AI), ML, and deep learning results in patient-specific diagnosis in real-time. Further developing this approach, startups and emerging companies develop algorithms to categorize clinical trial cohorts for improving drug discovery.
Israeli startup InnVentis develops a technology platform for drug discovery. With the help of real-world reference databases and ML algorithms, the startup enables the data collection at the molecular level and improves upon existing subjective patient surveys. This results in deriving actionable insights from the biological data. Their current focus is on rheumatoid arthritis.
Kheiron Medical Technologies – Deep Learning For Radiology
Identifying a malignancy in its early stages allows doctors to provide timely diagnosis and prevent it from becoming anymore harmful. Startups develop deep learning algorithms to study radiology reports such as mammograms and identify potential malignancies in patients as early as possible.
British startup Kheiron Medical Technologies provides deep learning algorithms for identifying malignancies. Combining radiology insights and deep learning methodologies, the startup enables medical professionals to detect and treat breast cancer much earlier than is possible with techniques currently in use for the same.
Arctic Fox AI – Deep Learning For Neurodegenerative Diseases
Technological advancements in machine learning, combined with biological data, equip medical professionals with scientific insights to better understand neurodegenerative diseases. Emerging companies help medical professionals by developing deep learning algorithms for treating the patient-specific ailments of the brain.
Canadian startup Arctic Fox AI offers deep learning algorithms for analyzing brain scans and provides meaningful insights into the patient’s neurological conditions. Further, the startup offers personalized advisory services for clinical trial designs by integrating various imaging biomarkers into its algorithms.
Intelligencia.ai – Risk Assessment For Clinical Trials
The success of a clinical trial depends on multiple variables across technical and regulatory parameters. These stringent parameters have to be fulfilled at every stage of the drug development process. The costs of clinical trials compared to pre-clinical research is also significantly higher. This drives startups to analyze and assess clinical trials for bringing drugs to the market faster.
The US-based startup Intelligencia.ai is working on ML algorithms to assess and help mitigate the risk during different phases of drug development. Moreover, the probability of technical and regulatory success (PTRS) is analyzed at each stage and helps in improving critical decision making. The startup also assesses the potential of drugs in-detail and identifies innovative trends in clinical research.
What About The Other 300 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 machine learning startups showcased above are promising examples out of 305 we analyzed for this article. To identify the most relevant solutions based on your specific criteria, get in touch.
