Advanced analytics and artificial intelligence (AI) are becoming increasingly important for the chemical industries. In the past, chemical companies were often relying on analytics tools that now appear fairly basic for making critical decisions within research and development (R&D), manufacturing, creation, supply chain and sales. Today, the ability of a chemical company to use advanced analytics & AI to inform those key decisions is becoming a competitive advantage vs. industry peers. AI use cases within the chemical industry are manifold. They range from predictive maintenance of manufacturing equipment to sales forecasting and marketing budget allocation to the smart steering of plants to reduce emissions. At the very beginning of the chemical value creation, during the discovery and formulation of new chemical compounds, AI technologies can help cut costs, discover superior chemical properties, accelerate time-to-market and improve product-market fits.

Machine Learning Algorithms Can Find the Needle in the Haystack of Available Molecules

Chemistry has many applications, from finding new drugs and vaccines, to finding better food flavours, more pleasant fragrances, or higher-quality materials. At its core, the field is commonly concerned with finding appropriate molecules, and often finding the best ways to assemble the resulting compounds into chemical formulas. Coming up with good compounds and formulas is a daunting task, because the number of combinations are nearly infinite. For example, it is estimated that there are about 10⁶⁰ drug-like molecules, whereas only about 10¹⁰ of these molecules are known (commercially or virtually); this means that so far we have explored about a quindecillion-th of a percent of possible molecules — a number ridiculously low (imagine 1 divided by 10⁵⁰). This is where AI and machine learning algorithms come into play: They excel in finding patterns in such haystacks. In the example of drug design, algorithms can help screening in advance the most promising drugs in instants, instead of relying on expensive and long trials. Even better: they can suggest brand new drugs that do not exist yet, but which are likely to be effective against certain diseases, while still being edible, soluble, etc. For these tasks, deep learning approaches based on recurrent neural networks, graph neural networks and transformer architectures are especially promising. For instance, such machine learning techniques are currently used for finding treatments and vaccines for COVID-19, via virtual screening or de-novo molecular generation, which are already showing promising results [1].

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