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Founded Year

2002

Stage

Loan | Alive

Total Raised

$32.88M

Last Raised

$1.5M | 3 yrs ago

About Verseon Corp

Verseon is the developer of a systematic, computationally-driven solution to achieve the molecular modeling accuracy necessary for rapid and cost-effective drug discovery. Verseon plans to use its platform to establish a diversified discovery pipeline across multiple disease areas. Current drug programs include anticoagulation, diabetic macular edema, and oncology (solid tumors). The company was founded in 2002 and is based in Fremont, California.

Headquarters Location

47071 Bayside Parkway

Fremont, California, 94538,

United States

510-225-9000

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Verseon Corp Patents

Verseon Corp has filed 24 patents.

The 3 most popular patent topics include:

  • Coagulation system
  • EC 3.4.21
  • G protein coupled receptors
patents chart

Application Date

Grant Date

Title

Related Topics

Status

3/27/2018

5/19/2020

Coagulation system, Coagulopathies, EC 3.4.21, Anticoagulants, Clusters of differentiation

Grant

Application Date

3/27/2018

Grant Date

5/19/2020

Title

Related Topics

Coagulation system, Coagulopathies, EC 3.4.21, Anticoagulants, Clusters of differentiation

Status

Grant

Latest Verseon Corp News

Why AI alone won’t resolve drug discovery challenges

Jun 21, 2023

Big Pharma and researchers are sharpening their focus on AI to speed drug discovery. But the path to fully AI-driven drug discovery faces substantial hurdles, according to Adityo Prakash, CEO of Verseon. “When it comes to drug discovery, AI has a data problem with which the pharmaceutical industry has not yet come to terms,” he said. Prakash explained there is simply “not enough data” to rely on AI as the primary means of small molecule drug discovery. He discussed these challenges further in an article in American Pharmaceutical Review titled “Exploring New Chemical Space for the Treatments of Tomorrow.” The limitations and challenges of AI-first methods Even with high-throughput screening to automate the process of testing pre-synthesized drug candidates against disease-associated target proteins, the pharma industry has managed to generate experimental data for fewer than ten million distinct chemicals out of a billion trillion trillion (1033 ) unique drug-like compounds synthesizable under the rules of organic chemistry. And “AI-first” methods can only be trained on that existing experimental data. It is like exploring a drop of water in an ocean, he said. In addition, even within that limited available experimental data, a sizable portion is of questionable quality and often not reproducible, he said. A 2022 Patterns article and a preprint on ArXiv reached similar conclusions regarding the need for high-quality data. To that list, these articles added the lack of interoperability and the curse of dimensionality. The latter problem refers to the fact that machine learning models require a large amount of data to accurately learn and make predictions. But as the dimensionality (number of features ) grows, the amount of data they require explodes. Another of Prakash’s core points is the lack of a particular data type critical for machine learning: negative data from lab experiments and clinical trials. In drug discovery, “failures” are published far less often than positive findings. “Negative data is as important as positive data for training an ML model,” he said. AlphaFold: A game of evolutionary guesswork It is tempting to believe that AI’s success in other areas will translate into success in drug discovery, especially when considering recent advances in the field. One of the most noteworthy AI tools to emerge in recent years is AlphaFold , a groundbreaking tool from DeepMind, which can accurately predict protein folding. Before AlphaFold debuted in 2021 , researchers relied on The Worldwide Protein Data Bank ( PDB ) to provide experimentally determined protein structures for about 90% of disease programs. AlphaFold augments the PDB data by predicting structures for those proteins where the structure was previously unknown. But the application of AI in such a complex field is not without its challenges. The  effectiveness of AI is partly a function of the quality and size of training sets. DeepMind actively designed AlphaFold to actively predict previously unknown protein structures, but DeepMind did not leave it to operate without actively providing sufficient foundational data. Several large databases offered vast numbers of known protein structures and their amino acid sequences across multiple species. This wealth of information, combined with various evolutionary rules that preserve the structure and function of proteins across species, provided a robust training ground for AlphaFold, playing a central role in its success, Prakash noted. Drug discovery is different from protein folding in ways that make it a far more daunting challenge for AI. Knowing a protein’s structure is a first step. But discovering new drugs requires understanding how that protein will bind to a novel drug structure. “But there is no binding data for truly novel compounds,” Prakash pointed out. And tools like AlphaFold cannot make these predictions . Understanding the challenges inherent in small molecule drug discovery Prakash provides an eye-opening statistic to illustrate the current limitations of AI in drug discovery, estimating that we only have available data on a 0.000000000000000000000001% of the drug-like chemical universe. “It is virtually impossible for current AI approaches to find breakthrough novel drugs unaided,” he explained. “And when you look at the companies who purportedly ‘discovered’ drugs with AI, you find that most have developed small ‘me-too’ modifications of well-trodden drug structures. Given the impossibly prohibitive time and cost involved in experimentally generating binding data for these novel drug structures, researchers must computationally derive the required data by simulating protein-drug interactions using highly accurate molecular-physics models. “Available garden-variety physics models won’t do,” Prakash said. Scientists need further advances in physics simulations to generate the data required to train next-generation AI models. Follow-up experimental data on promising novel structures can then further enhance these AI models, allowing researchers to systematically design and optimize novel drugs. Ultimately, AI is one tool of many, not a cure-all, Prakash concluded. Thoughtfully integrating advances in AI, physics, chemistry and biology is required to explore the ‘uncharted chemical ocean’ of potential new small molecule drugs. “Small molecule drug discovery requires progress across diverse fields and smart application of the resulting integrated tools,” he noted. “AI is not magical,” Prakash asserted. “We must understand where it’s valuable and where it’s not.” In reflecting on the future of drug discovery in the era of fast-moving technology, he concluded, “If you don’t use AI, you’ll be left behind,” Prakash concluded. “But the key to success is building and using all the other complementary tools required to generate the required high-quality data. AI acts on data.” Brian Buntz The pharma and biotech editor of WTWH Media, Brian is a veteran journalist with more than 15 years of experience covering an array of life science topics, including clinical trials, drug discovery and development and medical devices. Before coming to WTWH, he served as content director focused on connected devices at Informa. In addition, Brian covered the medical device sector for 10 years at UBM. At Qmed, he overhauled the brand’s news coverage and helped to grow the site’s traffic volume dramatically. He had previously held managing editor roles on two of the company’s medical device technology publications. Connect with him on LinkedIn or email at [email protected] . Tell Us What You Think!

Verseon Corp Frequently Asked Questions (FAQ)

  • When was Verseon Corp founded?

    Verseon Corp was founded in 2002.

  • Where is Verseon Corp's headquarters?

    Verseon Corp's headquarters is located at 47071 Bayside Parkway, Fremont.

  • What is Verseon Corp's latest funding round?

    Verseon Corp's latest funding round is Loan.

  • How much did Verseon Corp raise?

    Verseon Corp raised a total of $32.88M.

  • Who are the investors of Verseon Corp?

    Investors of Verseon Corp include Paycheck Protection Program.

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