Editing Ai Drug Discovery (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Drug discovery is a needle-in-a-haystack problem: chemical space is estimated to contain 10^60 drug-like molecules. Testing even a tiny fraction experimentally is impossible. AI reduces this search space by learning from known active compounds which molecular features predict activity.

'''Structure-based drug design with AlphaFold''': Traditionally, designing drugs required knowing the 3D structure of the target protein, obtained expensively by X-ray crystallography or cryo-EM. AlphaFold predicts protein structures computationally, opening structure-based design for thousands of previously undruggable targets. AI docking programs then predict how candidate molecules bind to the predicted structure.

'''Molecular GNNs for property prediction''': Molecules are naturally represented as graphs — atoms as nodes, bonds as edges. GNNs trained on datasets of molecules with measured properties (toxicity, solubility, activity) can predict properties of new, untested molecules. Models like MPNN, SchNet, and DimeNet achieve near-experimental accuracy for some properties.

'''Generative molecular design''': Rather than screening from a library, generative models design novel molecules with desired properties. Approaches include: VAEs (encode known drugs to latent space, generate new molecules by sampling/interpolating), RL (reward molecules with target properties), and diffusion models (DDPM on molecular graphs). DeepMind's AlphaFold3 and RFDiffusion can even generate protein sequences that fold to desired binding shapes.

'''Drug repurposing with knowledge graphs''': Building graphs connecting drugs, targets, diseases, genes, and pathways, then using GNNs to predict new drug-disease links. Baricitinib (originally for arthritis) was identified as a potential COVID-19 treatment through AI repurposing and subsequently validated.
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