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19 Online Marketplaces Facilitating Life Science Research

   by Andrii Buvailo    16793
19 Online Marketplaces Facilitating Life Science Research

(Last updated: 23.08.2018)

Online marketplaces are websites with a “many-to-many” business logic. They can host multiple suppliers trading with multiple buyers via different e-commerce tools available as a part of a website functionality.

Why are online marketplaces great?

Online marketplaces can provide a substantial added value to its users. For example, buyers can quickly compare and select better offerings without the need to research multiple websites and surf online for price comparisons or product specifications. Additionally, marketplaces bring more transparency, trust, and standardization to the whole process of sourcing.

[Interview] This Vancouver-based Startup Plans To Boost Drug Design With AI

   by Andrii Buvailo    922
[Interview] This Vancouver-based Startup Plans To Boost Drug Design With AI

Variational AI is a newly formed artificial intelligence (AI)-driven molecule discovery & drug design startup out of Vancouver, British Columbia, Canada. The company has developed Enki, an AI-powered small molecule discovery service. 

The founders of Variational AI are planning to build on top of their state-of-the-art expertise in machine learning, reflected in more than 40 research publications, including those presented at NIPS/NeurIPS, ICML, ICLR, CVPR, ICCV, and other top events in the area of artificial intelligence research.

11 Startups Using Quantum Theory To Accelerate Drug Discovery

   by Andrii Buvailo    4088
11 Startups Using Quantum Theory To Accelerate Drug Discovery

Molecular mechanics (MM) is a traditional computational approach when it comes to modeling in synthetic organic chemistry, medicinal chemistry and versatile aspects of drug design. However, MM methods have significant limitations, for example, when used to study electron-based properties within the drug-receptor microenvironment. Quantum mechanical (QM) methods allow to substantially increase the accuracy of predictions and provide much more relevant models of chemical and biological objects and their interactions, but QM methods are extremely (often prohibitively) computationally costly.

However, a series of advancements over recent years allowed to expand horizons in this direction, for example, the emergence of density functional theory (DFT), the overall increase in the computation power and the emergence of distributed cloud-based computational infrastructures.

Introducing Sysrev: The Intelligent Platform For Document Review And Automated Data Extraction

   by Thomas Luechtefeld    722
Introducing Sysrev: The Intelligent Platform For Document Review And Automated Data Extraction

In today’s technological world, data is perhaps the single most important driver of a business’ success. Access to relevant data allows businesses to make a variety of informed decisions. Unfortunately, acquiring this data can be quite cumbersome as employees spend countless hours manually reviewing documents. This is especially true for more complex reviews such as journal publications, patient records, or technical specifications. Sysrev offers enterprise a platform for managing collaborative document reviews, injecting machine learning into the review process to increase accuracy and efficiency. Depending on the data source and task, Sysrev can even automate data extraction.

Sysrev, launched in June 2019, is an intelligent platform for document reviews and automated data extraction. Sysrev optimizes the review process with machine learning and adds efficiency through its intuitive, and collaborative, interface.

Chemchart Enterprise: an Intelligent Platform for Chemical Data Management

   by TJ Bozada    601
Chemchart Enterprise: an Intelligent Platform for Chemical Data Management

Chemical data management is an important process to a number of industries, especially those engaged in manufacturing or research and development.  Unfortunately, chemical data is as unwieldy to manage as it is important.  This is for a variety of reasons, but the biggest contributing factor is the sheer amount of data available to the public and managed by an enterprise.  For decades, chemical data has been recorded in paper, and then excel sheets, and now databases.  While efforts have been made to homogenize, or at least centralize this data, there is not one single solution for indexing and searching the wide variety of data types, and sources, managed by an individual enterprise.  Chemchart Enterprise changes this paradigm by combining the flexibility of big data with the precision of machine learning, providing a single solution for managing the entire organization chemical space.