AI models in drug discovery are fundamentally flawed black boxes that researchers shouldn't trust. These algorithms understand nothing about chemistry and make purely statistical correlations without a scientific basis. Most AI models excel only on retrospective benchmarks and rarely deliver actual prospective value in discovering real drug leads. As AI models often overfit datasets and struggle in real human systems, claims of restoring health in diseased cells may be premature without rigorous wet‑lab and clinical tests.
Harvard's PDGrapher AI model is a breakthrough tool that actually reverses disease in cells by precisely targeting multiple pathways simultaneously. Unlike traditional single-target approaches, this advanced tool identifies the optimal gene combinations to restore healthy cell function. It has already validated known cancer targets and discovered new, previously overlooked ones with 35% greater accuracy than competing models. It offers a strong promise to significantly accelerate drug discovery — especially for complex diseases like cancer.
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