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Pepticom set to develop AI models enabling peptide-based drug discovery

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The Israeli company announced the closing of its Series A funding at $5m (€4.49m) from the Chartered Group investment firm, which is expected to use the funds to enable further development of artificial intelligence (AI) models that can be used in peptide drug discovery. According to the company, the pharmaceutical industry has recently shown an increased interest in peptide R&D, leading to a resurgence of peptide drug candidates for various indications, since the molecules can be'highly selective and efficacious, as well as relatively safe'. The AI technology developed by Pepticom aims to streamline and accelerate researcher's discovery of peptide-based drug candidates, compared to traditional methods which are'costly and time consuming', the company stated. Discovery is achieved "by searching an enormous set of possible solutions, vastly reducing the risk of failure during development." More specifically, the technology covers a chemical space of 1,030 possible molecular options, which is larger than current screening techniques.


Adapting drug discovery to Artificial Intelligence

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Posted: 25 July 2018 Ami S Lakdawala (GSK's In-silico drug discovery unit), George Okafo (GSK's In-silico drug discovery unit), John Baldoni (GSK's In-silico drug discovery unit), Michael Palovich (GSK's In-silico drug discovery unit), Tobias Sikosek (GSK's In-silico drug discovery unit), Voshal Sahni (GSK's In-silico drug discovery unit) No comments yet Drug discovery has always been challenging; today, more so than ever. While there has been success in addressing many diseases, others remain intractable. There is a need and opportunity to explore new drug discovery approaches that harness immense datasets (public and private), which have been built upon the successes and failures of the past to guide in-silico approaches to new therapies. Advances in genetics and molecular biology have revealed potential new targets for developing medicines. Deciding which target to pursue is challenging and an area in which there is opportunity to increase productivity.


AI in Drug Discovery Querter 4 / 2018

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This 100-page report marks the fourth installment in a series of reports on the topic of the Artificial Intelligence in Drug Discovery Industry that Deep Knowledge Analytics have been producing for more than 1 year now. We released the first edition of these reports, entitled "AI for Drug Discovery Landscape Overview 2017", in the end of 2017, followed by "AI for Drug Discovery & Advanced R&D Q1 2018" in the first quarter of 2018, and "AI for Drug Discovery & Advanced R&D Q2 2018" in the second quarter of 2018. The present edition consists of an updated overview of the state of the industry in Q2 of 2018, tuned to the latter half of 2018 and including extended coverage of major events in Q4 of 2018. It revisits the major insights, trends, data analytics, conclusions and forecasts of our previous report, analyzing which trends and conclusions are still on track, which ones have changed course, and which ones have been usurped by entirely new insights, trends and conclusions.


How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.


How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from stating the problem, exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.