omniture

Standigm cited as 'Top 10 Generative AI-based startup based on VC Investment, 2019-2021' in Gartner® report

2023-01-05 22:00 1766

SEOUL, South Korea, Jan. 5, 2023 /PRNewswire/ -- Standigm, a company using artificial intelligence (AI) technology for drug discovery and development, today announced that it had been cited in the 2022 Gartner Emerging Tech: Venture Capital Growth Insights for Generative AI research.

According to Gartner, "2021 saw a huge jump of 180% to $840 million in the total investment in AI-assisted drug discovery, from $299 million in 2020." About half that capital was invested in AI-assisted drug discovery startups. Standigm ranked seventh of 10 Generative AI-based startups based on its level of VC funding.

What generative AI is, and how it's used in drug discovery

Generative AI uses generative adversarial networks (GANs) to generate data. Once trained on labeled data, such as cars and buses, it can use its learnings to produce data about other transportation mechanisms it has never seen. In their report, Gartner predicted, "By 2025, 30% of marketing content will be generated by AI, from less than 2% in 2022."

In the drug discovery context, Generative AI efficiently designs drugs by learning the relationship between the correct answer and the candidate's material structure. It then creates new structures that have never existed before and partially known but improved structures. It also identifies areas yet to be evaluated as drugs or structures that have proven less reliable.

How Standigm's BEST platform makes drug discovery efficient

The BEST platform uses a proprietary scaffold replacement technology that provides the most significant advantages for molecular novelty, patentability, and backup plans. As it provides various generative models with different levels of structural modification from atoms to scaffolds, it can support all the steps of drug discovery where molecular changes should be made; hit identification, hit-to-lead, and lead optimization. Combining it with advanced activity/drug property prediction models and synthetic AI models enables obtaining many lead series molecules, which elevates the productivity of early drug discovery projects.

GARTNER is the registered trademark and service mark of Gartner Inc., and/or its affiliates in the U.S. and/or internationally and has been used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About Standigm

Standigm is a drug discovery company that searches therapeutic lead compounds by using advanced artificial intelligence (AI) trained on biomedical big data. We design lead molecules and predict targets and pathways of untreatable diseases and find new indications of existing drugs with the ensemble of 8 different AI models and a comprehensive graphic knowledge database.

Founded in 2015 by experts in artificial intelligence and systems biology at Samsung Advanced Institute of Technology, Standigm has grown into a team of elite researchers, composed of 50% PhDs with multi-disciplinary expertise in chemistry, biology, pharmacology, and high-performance algorithms and data structures.

Our vision is a full-stack pharmaceutical company that could ease the pains of patients all over the world. For more information, please, visit: www.standigm.com

Media Contact

Standigm PR
prforstandigm@bospar.com

Cision View original content to download multimedia:https://www.prnewswire.com/news-releases/standigm-cited-as-top-10-generative-ai-based-startup-based-on-vc-investment-2019-2021-in-gartner-report-301714001.html

Source: Standigm
collection