Editing AI in Natural Sciences (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing an AI-accelerated scientific discovery pipeline:

'''1. Drug discovery pipeline'''
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Target identification: which protein is implicated in disease?
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[AlphaFold: predict target protein 3D structure if not experimentally known]
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[Binding site prediction: fpocket, SiteMap → identify druggable cavities]
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[Virtual screening: dock 10M+ compounds from ZINC database (AutoDock Vina)]
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[ADMET prediction: GNN model trained on known compounds]
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[Generative design: DiffSBDD generates novel molecules optimized for target]
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[Multi-objective optimization: maximize binding affinity, minimize toxicity, maximize solubility]
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[Synthetic accessibility filter: retrosynthesis prediction (ASKCOS, AiZynthFinder)]
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[Top 100 candidates → experimental wet-lab validation]
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[Active learning: feed experimental results back to retrain model]
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'''2. Climate emulator design'''
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Full physics-based climate model runs (CESM, GFDL-CM4) for training data
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[Neural operator (FNO, ViT-based) learns input → output mapping]
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[Physics constraints: conservation laws as auxiliary losses]
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[Uncertainty quantification: ensemble or probabilistic output]
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[10,000× speedup: emulator runs in seconds vs. weeks for full model]
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[Use for: uncertainty analysis, scenario exploration, climate attribution]
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'''3. Active learning for expensive experiments'''
* Gaussian Process surrogate: fits to sparse experimental results, provides uncertainty estimates
* Acquisition function: select next experiment maximizing expected improvement
* Bayesian optimization loop: iterate until target property achieved or budget exhausted
* Applications: materials property optimization, drug candidate selection, experimental design

[[Category:Artificial Intelligence]]
[[Category:Natural Sciences]]
[[Category:Deep Learning]]
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