Drug target identification connects a disease mechanism with a candidate intervention point, such as a protein, gene or pathway. Researchers combine biological knowledge, patient data and experiments to judge whether changing that target could improve an outcome.
Choosing the wrong target can make excellent molecule design irrelevant. Models can help integrate evidence and rank candidates, but causal relevance and behavior in a complex human organism require experimental and clinical validation.
A plausible target may not cause the disease process or may have unacceptable effects elsewhere, and those relationships can be difficult to establish from limited data.
Can AI choose a drug target from scientific text alone?
Text can support hypothesis generation, but experimental and clinical data are needed to test causal biology and relevance to patients.
