Artificial intelligence, “building-block” chemistry and a molecule-making machine teamed up to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials research – a finding that could speed innovation and drug discovery as well as make complex chemistry automated and accessible.
One of the biggest challenges in traditional laboratory settings is performing countless hours of error-prone lab work: handling reagents, incubating reactions or living cells, synthesizing products, applying treatments, and monitoring outcomes. The Illinois Biological Foundry for Advanced Biomanufacturing was established in 2014 to bypass these cumbersome procedures and support a broad array of research goals.
In bioinformatics, machine learning (ML) tools are used to solve problems in molecular biology and genetics. In healthy cells, genes — the carriers of hereditary information — are switched “on” or “off” to carry out specific tasks. Bioinformatics researchers can decode DNA using artificial intelligence (AI) to understand why some of these switches occur inappropriately, leading to disease.
Artificial intelligence is often employed in the field of cancer genomics, where bits of DNA sequencing data must be identified and further analyzed with statistical, evolutional, and probabilistic models. “Off-the-shelf” computing tools are useful for many cancer researchers, but Mohammed El-Kebir (IGOH), Cancer Center at Illinois (CCIL) scientist, is taking these AI applications a step further.
A team led by Steven L. Miller Chair professor of chemical and biomolecular engineering Huimin Zhao (BSD leader/CABBI/MMG) was awarded a five-year $20 million grant from the National Science Foundation (NSF) for the NSF Artificial Intelligence (AI) Institute for Molecular Discovery, Synthetic Strategy and Manufacturing (Molecule Maker Lab Institute or MMLI).
