How your data is organized and stored, and the data relationships within, is defined by a data model. An effective model allows users across your organization to easily understand how the business operates. It is the linchpin of almost every high-value business solution, with its greatest value realized when applied beyond the boundaries of individual lines of businesses (LOBs) or operations within an organization. This data model is a strategic pillar for information management, upon which the success of future business-critical projects depend.
Though an increasing number of novel chemical compounds are being synthesized each year, there is growing concern that innovation may be stagnating in small molecule discovery. However, new research published by CAS scientists in the October 2019 Journal of Organic Chemistry reveals that the pace of small molecule innovation is actually accelerating from a structural perspective and offers insights into finding fruitful new areas for further investigation as chemists navigate a vast and largely unexplored chemical space.
The promise of artificial intelligence has always felt more like a future state, but the reality is that many companies are already adopting AI initiatives. This is especially true in the scientific R&D realms. Over the last few years, there has been a huge increase of machine learning and AI initiatives in everything from QSAR models to genomics. According to a 2018 survey, AI adoption grew drastically from 38% in 2017 to 61% in 2018. This occurred across a variety of industries, including healthcare, manufacturing and financial services. However, most early adopters noted one of the biggest challenges to successful implementation involved data, specifically, accessing, protecting, integrating and preparing data for AI initiatives.
Given the wide range of applications and benefits offered by ML, there is a current push to implement it in the materials science sector, with many R&D-based organizations investing heavily in the development of digital strategies. However, one challenge these teams face is that scientific data is often complex and disconnected. This is a problem because ML systems rely on well-organized, high-quality data. So, how can you effectively apply ML to accelerate innovation and growth in your materials science company?
About a year ago, I applied for CAS Future Leaders—an international program that aims to develop leadership skills and expand the professional network of Ph.D. students and postdoctoral researchers. As a grad student in my final year, I was especially excited to meet fellow scientists from similar international backgrounds