Chemspace and ChemAlive (the largest repository of quantum chemical data, a Contract Research Company that develops software products to obtain accurate and reliable data on molecular properties, synthetic reactions, verification of molecules using quantum chemistry), announced the launch of the collaboration to provide accurate molecular structural information about commercial small molecules.
About one in three human proteins is understudied. Even when quantifying data is available from multiple sources, "dark" genes and proteins are simply not well characterized (Figure 1).
Are you curious where dark gene hunting leads? There are a number of resources:
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.
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.
In 1970-80s, the idea of virtual screening was regarded as a conceptual way to substitute costly and time-consuming experimental “screen-everything-you-have” approaches with a much faster and cheaper predictive modelling to cherry-pick only the best molecules for subsequent synthesis and validation in a lab. A great number of computational tools and approaches emerged, aiming at “pre-screening” new promising molecules, so called “hits”, or augmenting experimental screening programs to optimize efforts.