The Internet media is trending now with numerous mentions of “big data”, “machine learning” and “artificial intelligence” all together destined to revolutionize pharmaceutical and biotech industries and the way drugs are discovered. These new technologies are believed to make drug discovery cheaper, faster, and more productive.
But how is “magic” supposed to happen, after all?
The state of data access, quality and dissemination in Chemistry is extremely poor - so poor that it is blocking advances in machine learning (ML) and artificial intelligence (AI), and also impeding research and development in traditional methods. The recent surge in AI skepticism is a direct consequence of years of over-hype and promises based on precarious data. Over-the-top expectation were offered without enough consideration for the data quality and volume required to train fancy algorithms. The old adage “^&$% in, ^&$% out” holds true (we can say ‘crap’ right?). This opinion is in line with recent statements by the CEO of Novartis, for example, who runs the second largest pharmaceutical company in the world, lamenting the difficulty in accessing quality datasets to make AI effective.
The digital transformation of biopharmaceutical manufacturing is continuing at a rapid pace as companies attempt to mine the sources of data available. Innovations include predictive analytics, big data analytics, and creating the digital plant. Digital transformation offers a mechanism to revise its business model, to improve production processes, to design new drugs faster by using artificial intelligence to screen compounds and to increase responsiveness to customers. Furthermore, the volume of data processed by pharmaceutical firms shows no sign of slowing down. This means pharmaceutical companies must act quickly in terms of building core internal digital capabilities and moving beyond their traditional IT functions to all areas of the business.
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.
