The pipeline splits into two kinds of work. Target identification and validation, along with the design and prioritization parts of lead optimization, are mainly computational and literature-driven: they run on data, models, and reasoning. Hit finding and screening, the assay rounds of lead optimization, and preclinical testing are physical: they require real molecules, real cells, and real instruments.
Can AI Run a Drug Discovery Lab on Its Own?
The Drug Discovery Pipeline in Plain Terms
Thinking Stages and Wet-Lab Stages
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Look at what those four stages actually demand. Deciding on a target is reasoning over literature and data. Screening a molecule library is not. It needs real compounds in real wells, and something has to measure what happens.
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