Dr. Ola Engkvist, AstraZeneca, Mölndal, Sweden
Artificial intelligence is underway to transform the society through technologies like self-driving cars. Also, in drug discovery machine learning and artificial intelligence methods has received increased attention. [1] The increased attention is not only due to methodological progress in machine learning and artificial intelligence, but also progress in automation for screening, chemistry, imaging and -omics technologies, which have generated very large datasets suitable for machine learning.
While machine learning has been used for a long time in drug design, there has been two exiting developments during the last years. One is the progress in synthesis prediction, where deep learning together with fast search methods like Monte Carlo Tree Search has been shown to improve synthetic route prediction as exemplified by a recent Nature article. [2] The second development, is applying deep learning based methods for de novo molecular design. It has always been the dream of the medicinal and computational chemist to be able to search the whole chemical space of estimated 1060 molecules. This would be a step change compared to search enumerable chemical libraries of perhaps 1010 compounds. Methods to search the whole chemical space through generative deep learning architectures has been developed during the last 3-years. The basis will be described and exemplified of how molecules are generated. After the concept has been introduced it will be described how the AI methods will synergize with progress in automation in general and especially chemistry automation.
