AI-driven automated chemistry as a tool to accelerate drug discovery
Typically, early drug discovery campaigns start with the screening of chemical libraries to identify candidate chemotypes modulating a particular target and/or phenotype. Success of the primary screening depends on multiple factors related to both biology and chemistry. These include the target’s druggability, sensitivity and specificity of assay system, composition and diversity of chemical libraries, number of screened compounds, etc.
SINCE every project has cost constraints, the chemical libraries cannot cover the entire chemical space of up to 1060 potentially drug-like small molecules.1 Moreover, chemical library size ranges are often between tens of thousands to hundreds of thousands, which further complicates the search for new prospective chemotypes in such narrow chemical space. The recent advances in chemistry automation could help to overcome this limitation and establish the process of new bioactive chemical discovery.
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