Drug discovery faces persistent challenges rooted in poor target selection, high late-stage attrition rates, and the challenge of converting signals from genome-wide association studies into discovery decisions. Many genetic variants linked to diseases are poorly understood, and AI tools are only as useful as the quality of the perturbation data they are trained on.
This Drug Target Review report brings together leading researchers to address these gaps. Contributors examine how combining scalable CRISPR screens, stem cell models, iPSC-derived neuronal systems, and regulatory RNA therapeutics strengthens early-stage target validation.
The report also explores how genome architecture analysis and end-to-end functional genomics strategies, supported by layered quality control, can convert genetic risk signals into confident therapeutic hypotheses
