Nat. Rev. Drug Discov. | 药物发现中的人工智能：内涵、现状与未来路径
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TL;DR - A Nature Reviews Drug Discovery perspective (Bender, Thomas, Scannell et al., 2026) critically reviews AI in drug discovery, arguing that technical capability has advanced far faster than clinical impact, and proposes shifting evaluation from model validation to whether models actually improve project decisions.
- Impact is misallocated: most AI work targets preclinical stages (hit finding) where labeled data is easy, while the biggest lever on cost-per-approved-drug is Phase II success — e.g. biomarker-based patient stratification roughly halves capitalized cost per launched drug ("looking under the streetlight").
- Data is the bottleneck, not modeling: biological labels are scarce, high-dimensional, confounded, and "epistemically opaque" — whether a compound "hits a target" depends on assay type and ATP concentration; proxy readouts like liver-organoid cytotoxicity correlate poorly with clinical DILI, and thermal-shift assays only marginally track enzyme inhibition.
- Chemical space is vast and local: real datasets cover a tiny biased fraction; the same functional group has different effects on different scaffolds, and four common ADME datasets share very few compounds or scaffolds, so applicability domains don't align for multi-objective optimization.
- Validation must be use-case-specific: selection (hit ranking), exclusion (tox/clearance triage), and quantitative prediction (dose) demand different metrics — two models with similar AUC can behave oppositely. AlphaFold is cited as model-validation success that did not automatically translate into drug-discovery-process success; the proposed path forward is human-relevant data generation (iPSC models, organ-on-chip, Cell Painting, perturbation atlases), preclinical–clinical data feedback loops including negative outcomes, and consortium-scale purpose-built datasets.
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Nat. Rev. Drug Discov. | 药物发现中的人工智能：内涵、现状与未来路径
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TL;DR - A Nature Reviews Drug Discovery perspective (Bender, Thomas, Scannell et al., 2026) critically reviews AI in drug discovery, arguing that technical capability has advanced far faster than clinical impact, and proposes shifting evaluation from model validation to whether models actually improve project decisions.
- Impact is misallocated: most AI work targets preclinical stages (hit finding) where labeled data is easy, while the biggest lever on cost-per-approved-drug is Phase II success — e.g. biomarker-based patient stratification roughly halves capitalized cost per launched drug ("looking under the streetlight").
- Data is the bottleneck, not modeling: biological labels are scarce, high-dimensional, confounded, and "epistemically opaque" — whether a compound "hits a target" depends on assay type and ATP concentration; proxy readouts like liver-organoid cytotoxicity correlate poorly with clinical DILI, and thermal-shift assays only marginally track enzyme inhibition.
- Chemical space is vast and local: real datasets cover a tiny biased fraction; the same functional group has different effects on different scaffolds, and four common ADME datasets share very few compounds or scaffolds, so applicability domains don't align for multi-objective optimization.
- Validation must be use-case-specific: selection (hit ranking), exclusion (tox/clearance triage), and quantitative prediction (dose) demand different metrics — two models with similar AUC can behave oppositely. AlphaFold is cited as model-validation success that did not automatically translate into drug-discovery-process success; the proposed path forward is human-relevant data generation (iPSC models, organ-on-chip, Cell Painting, perturbation atlases), preclinical–clinical data feedback loops including negative outcomes, and consortium-scale purpose-built datasets.