September 3, 2019, London, UK: Deep Knowledge Analytics acknowledges the new ground-breaking achievement of Insilico Medicine, which succeeded to design, synthesize and validate a novel drug candidate end-to-end in just 46 days, which is 15 times faster compared to what it typically takes even for the top pharma corporations capable to conduct the most efficient R&D process.

While it typically takes 2-3 years to go from initial drug discovery to preclinical validation, one AI for Drug Discovery company has done the same in less than 2 months end-to-end, which is 15 times faster compared what it typically takes even for the best pharma corporations capable to conduct the most efficient R&D process.

September 3rd, 2019, London, UK: Deep Knowledge Analytics salutes its parent company, Deep Knowledge Ventures, in the landmark Nature Biotechnology publication of its portfolio company, Insilico Medicine, demonstrating the design, synthesis and preclinical validation of a novel drug candidate in just 46 days.

Deep Knowledge Analytics also released a report examining AI, which examined practices at 50 pharmaceutical companies to identify leaders who are most actively driving the use of AI in their organizations.

​Importance of senior management

​The goal of this research is to benchmark the impact of management support on the use of AI and overall efforts to increase R & D efficiency, according to Margaretta Colangelo, managing partner at Deep Knowledge Ventures. 

On October 26, 2018, Dmitry Kaminskiy, Founding Partner of Deep Knowledge Ventures, presented keypoints from the landmark 2200 page AI Industry in UK Q3 2018 report jointly produced by Big Innovation Centre and Deep Knowledge Analytics in coordination with the All-Party Parliamentary Group (APPG) on AI, alongside Lord Clement-Jones, CBE (Co-Chairman of the APPG on AI) and Birgitte Andersen (CEO of BIC), at a meeting convened to announce the report’s publication.

On 28-29 May in London, global pharma leaders gather at the 2nd Annual meeting dedicated to Artificial Intelligence and Machine Learning for Advanced Drug Discovery and Development to exchange experience and share knowledge through panel discussions, practical case studies, interactive sessions. Delegates will have the opportunity to pose their biggest questions directly to industry experts in small working groups on the topic of their choice during Choose & Join interactive roundtable discussions.

Members of the MIT engineering faculty receive many awards in recognition of their scholarship, service, and overall excellence. Every quarter, the School of Engineering publicly recognizes their achievements by highlighting the honors, prizes, and medals won by faculty working in our academic departments, labs, and centers.

Regina Barzilay, Manolis Kellis, Tommi Jaakola and Peter Szolovits were named among “Top 100 AI Leaders in Drug Discovery and Advanced Healthcare” by Deep Knowledge Analytics on Feb. 1.

The AI for Drug Discovery space is evolving rapidly. Taking into account the progress in the last three years, it's reasonable to expect that really significant results could be achieved in the next three years. In the first quarter of this year, the number of new research centers increased by 10, the number of companies in the space increased by 20, and the number investment funds investing in the space increased by 30. 

China-based pharmaceutical companies betting big and small biopharma able to quickly innovate will drive the use of AI for drug discovery – a market some analysts predict will reach a valuation of $20bn by 2024...

The level of sophistication used in due diligence should be on a par with the level of complexity in a given industry. AI-Pharma companies are 100 times as complex as FinTech companies. Methodologies used to assess them should be 100 times as rigorous.

Discovering new drugs using AI is one of the most challenging areas in biological sciences...

Deep Knowledge Analytics picked the 'Top 100 AI Leaders in Drug Discovery and Advanced Healthcare’ out of an initial pool of 500 outstanding candidates. Unless a new AI winter wind blows and sweeps over the science explorers, their work is quite likely to enhance the quality of life surpassing the most vivid science fiction imagination...

Although pharmaceutical companies spend over $172 billion on research and development annually, over 90% of molecules discovered using traditional techniques fail in human clinical trials. Moreover, 75% of newly approved drugs are unable to cover the cost of development and some analysts predict that ROI in pharmaceutical R&D may hit zero by 2020...

This week four CSAIL faculty were named among the top 100 global leaders in artificial intelligence for health, according to a new report developed by a top technology think-tank.

Deep Knowledge Analytics’s “Top 100 AI Leaders in Drug Discovery and Advanced Healthcare” (PDF) looked at scientists, clinicians and technologists across academia, pharma, and AI companies...

A recent article about AI-based drug development, indicates the progressive use of A.I. technologies for the drug production industry, mining the huge amount of data collected from the pharmaceutical companies’ data centers. Thanks to Deep Knowledge Ventures which a couple of days ago updated their research data, we took the opportunity to delve into the latest developments of this market...

Billions are being bet to find the next revolutionary drug. In 2013, US start-up Berg was asked by the Department of Defense to help improve the detection of prostate cancer, a disease common among pilots. Less than five years later, an artificial intelligence-driven tool has been tested on more than 1,000 patients with promising results...

“China’s ability to generate massive amounts of real-time medical data from more than 1 billion people in the local population, stimulated by rather flexible privacy laws is driving advancement,” writes Margaretta Colangelo of Deep Knowledge Ventures. Though she says that government programs are also helping to grow local AI and data science talent, Colangelo also warns that China is currently lagging behind its competitors in pharma and biotech innovation, which could limit its progress.