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Dotmatics

Founded Year

2005

Stage

Acquired | Acquired

Valuation

$0000 

About Dotmatics

Dotmatics is a global informatics solution and service provider that provides tools for knowledge management, data storage, enterprise querying and reporting, data analysis and visualization. On March 22nd, 2021, Dotmatics was acquired by Insightful Science.

Headquarters Location

The Old Monastery, Windhill Bishops Stortford

Herts, England, CM23 2ND,

United Kingdom

+44(0)1279 654 123

Missing: Dotmatics's Product Demo & Case Studies

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ESPs containing Dotmatics

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

EXECUTION STRENGTH ➡MARKET STRENGTH ➡LEADERHIGHFLIEROUTPERFORMERCHALLENGER
Industrials / Manufacturing Tech

Process management systems provide end-to-end management services for design and development. They help document process knowledge, best practices, and ideas. These solutions can also enable knowledge sharing between scientists, engineers, and management. Vendors in the space provide interfaces for process knowledge capture, workflow management, and collaboration. The technology is similar to digi…

Dotmatics named as Leader among 5 other companies, including MakerOS, Sapio Sciences, and Fluence Analytics.

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Expert Collections containing Dotmatics

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Dotmatics is included in 1 Expert Collection, including Advanced Manufacturing.

A

Advanced Manufacturing

2,661 items

Latest Dotmatics News

The role of artificial intelligence in drug discovery

Sep 1, 2022

Research & Development World Haydn Boehm, Director of Product Marketing, Dotmatics Despite the buzz around artificial intelligence (AI), most industry insiders know that the use of machine learning (ML) in drug discovery is nothing new. For more than a decade, researchers have used computational techniques for many purposes, such as finding hits, modeling drug-protein interactions, and predicting reaction rates. What is new is the hype. As AI has taken off in other industries, countless start-ups have emerged promising to transform drug discovery and design with AI-based technologies for things like virtual screening, physics-based biological activity assessment and drug crystal-structure prediction. Investors have made huge bets that these start-ups will succeed. Investment reached $13.8 billion in 2020. And, more than one-third of large-pharma executives report using AI technologies. While a few “AI-native” candidates are in clinical trials, around 90% remain in discovery or preclinical development, so it will take years to see if the bets pay off. Artificial expectations Along with big investments comes high expectations — drug the undruggable, drastically shorten timelines, virtually eliminate wet lab work. Insider Intelligence projects that discovery costs could be reduced by as much as 70% with AI. Unfortunately, it’s just not that easy. The complexity of human biology precludes AI from becoming a magic bullet. On top of this, data must be plentiful and clean enough to use. Models must be reliable. Prospective compounds need to be synthesizable. And drugs must pass real-life safety and efficacy tests. While this harsh reality hasn’t slowed investment, it has led to fewer companies receiving funding, to devaluations and to discontinuation of some more lofty programs, like IBM’s Watson AI for drug discovery. This begs the question: Is AI for drug discovery more hype than hope? Absolutely not. Do we need to adjust our expectations and position for success? Absolutely, yes. But how? Three keys to implementing AI in drug discovery Implementing AI in drug discovery requires reasonable expectations, clean data and collaboration. Let’s take a closer look. Reasonable Expectations AI can be a valuable part of a company’s larger drug discovery program. But, for now, it’s best thought of as one option in a box of tools. Clarifying when, why and how AI is used is crucial, albeit challenging. Interestingly, investment has largely fallen to companies developing small molecules, which lend themselves to AI because they’re relatively simple compared to biologics, and because there are decades of data upon which to build models. [1,2] There is also great variance in the ease of applying AI across discovery, with models for early screening and physical-property prediction seemingly easier to implement than those for target prediction and toxicity assessment [3,4]. While the potential impact of AI is incredible, we should remember that good things take time. Pharmaceutical Technology recently asked its readers to project how long it might take for AI to reach its peak in drug discovery, and by far, the most common answer was “more than nine years.” Clean Data “The main challenge to creating accurate and applicable AI models is that the available experimental data is heterogenous, noisy and sparse, so appropriate data curation and data collection is of the utmost importance.” This quote from a 2021 Expert Opinion on Drug Discovery article speaks wonderfully to the importance of collecting clean data. While it refers to ADEMT and activity prediction models, the assertion also holds true in general.AI requires good data, and lots of it. But good data are hard to come by. Publicly available data can be inadequate, forcing companies to rely on their own experimental data and domain knowledge. Unfortunately, many companies struggle to capture, federate, mine, and prepare their data, perhaps due to skyrocketing data volumes, outdated software, incompatible lab systems, or disconnected research teams. Success with AI will likely elude these companies until they implement technology and workflow processes that let them: – Facilitate error-free data capture without relying on manual processing – Handle the volume and variety of data produced by different teams and partners – Ensure data integrity and standardize data for model readiness Collaboration Companies hoping to leverage AI need a full view of all their data, not just bits and pieces. This demands a research infrastructure that lets computational and experimental teams collaborate, uniting workflows and sharing data across domains and locations. Careful process and methodology standardization is also needed to ensure that results obtained with the help of AI are repeatable. Beyond collaboration within organizations, key industry players are also collaborating to help AI reach its full potential, making security and confidentiality key concerns. For example, many large pharmas have partnered with start-ups to help drive their AI efforts. Collaborative initiatives, such as the MELLODDY Project, have formed to help companies leverage pooled data to improve AI models. Haydn Boehm is Director of Product Marketing at Dotmatics , a leader in R&D scientific software connecting science, data, and decision-making. Its enterprise R&D platform and scientists’ favorite applications drive efficiency and accelerate innovation.

Dotmatics Frequently Asked Questions (FAQ)

  • When was Dotmatics founded?

    Dotmatics was founded in 2005.

  • Where is Dotmatics's headquarters?

    Dotmatics's headquarters is located at The Old Monastery, Windhill, Herts.

  • What is Dotmatics's latest funding round?

    Dotmatics's latest funding round is Acquired.

  • Who are the investors of Dotmatics?

    Investors of Dotmatics include Dotmatics (acquired by Insightful Science), Scottish Equity Partners and Merck & Co..

  • Who are Dotmatics's competitors?

    Competitors of Dotmatics include Sapio Sciences and 4 more.

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Sapio Sciences specializes in laboratory information management system software (LIMS) which allows scientists to quickly configure the LIMS to their requirements. It markets the exemplary product line which includes laboratory information management (LIMS), electronic laboratory notebook (ELN), molecular diagnostics (MDx), biomarker discovery, and data management in a single platform. The company was founded in 2004 and is based in Baltimore, Maryland.

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Fluence Analytics

Fluence Analytics is a manufacturer of industrial and laboratory monitoring systems that produce continuous data streams. These measurements enable real-time optimization of process control and faster R&D for polymer and biopharmaceutical manufacturers.

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Synthace

Synthace aims to raise universal bioscience productivity, enabling people to better engineer biology for health, food, energy and manufacturing. Central to its technology is Antha, an operating system for biology that enables ease of designing and optimizing biological unit operations that are linked into executable workflows that are reliable, shareable and saleable.

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Opvia

Opvia develops flexible spreadsheet software that simplifies lab management and experimental processing through automation and reusable protocols. It provides a flexible, open platform built for scientists and ensures structured experimental data, analytics and lab management are all in one place.

Dotmatics (acquired by Insightful Science) Logo
Dotmatics (acquired by Insightful Science)

Dotmatics is an R&D scientific software connecting science, data, and decision-making. In March of 2021, Dotmatics joined forces with Insightful Science through a merger. In April 2022, the two companies consolidated under the Dotmatics brand. The company’s principal office is in Boston, with offices and R&D teams located around the world.

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