Cell：生成式AI真正理解生命，还要解决的十五大挑战

TL;DR - A Cell perspective outlines 15 challenges that generative AI must overcome to progress from molecular successes such as protein modeling to reliable predictions of cellular behavior and clinical outcomes. It argues that scarce perturbation data, complex biological interactions, and sequence-centric architectures require biology-informed models and prospective evaluation.

• The challenges span molecular networks, synthetic biology, cell-state control, biomarkers, drug toxicity and efficacy, immune responses, and clinical-trial outcomes.
• The authors propose incorporating biological priors, such as protein-interaction and gene-regulatory networks, to constrain models and compensate for limited data.
• Current benchmarks often reward statistically significant gains on already-solved or retrospective tasks; the article calls for prospective experimental validation and CASP- or DREAM-style blind evaluations.
• Progress will require challenge-specific datasets, clinical data sharing, diverse cohorts, and sustained collaboration among computational scientists, experimentalists, clinicians, and ethicists.