Innovation is the name of the game for chemical companies to optimize operations and enhance profits. To fast-track innovations, AI and Machine Learning in the chemical industry are crucial.
AI aids drug discovery to deliver new medications to market faster. AI-assisted synthesis planning and chemical data analysis can help chemists be more productive in their daily work.
The synthesis of molecules remains one of the most critical challenges in organic chemistry. The standard approach to solving a problem is based on experience. It constitutes repetitive synthesis experiments, often resulting in non-optimal solutions. AI dramatically speeds up the discovery of a reliable reaction pathway that leads to the target compound from a given set of commercially available compounds.
Classical molecular design involves multiple iterations of experimental verification that the molecule possesses the required properties. This process takes a lot of effort, time, and materials. AI can generate drug candidates according to the preset molecular properties like solubility, toxicity, bioactivity, etc.
Chemical properties prediction
Predicting the properties of drug candidates enables researchers to synthesize only the most promising molecules and skip low-ranked candidates.
Chemical data analysis
Chemical industry works with molecular structures, niche literature, crystallography, NMR, and thermophysical data. NMR and LC-MS data, for example, correlate chemical structures with spectrums. AI offers improved automatic compound detection from spectrograms. Another example is a combination of NLP and image processing for automatic extraction of chemical reactions and conditions from academic papers and patents.
Companies that implemented AI in the chemical sector report a 2x to 5x improvement in one or more process KPIs