Editing Ai Natural Sciences (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''AlphaFold''' — DeepMind's AI system that predicts the 3D structure of proteins from amino acid sequences, considered one of the most significant scientific achievements of AI to date.
* '''Protein structure prediction''' — Determining the 3D shape a protein folds into from its linear amino acid sequence — a problem that took decades to solve and has massive implications for drug discovery.
* '''Molecular dynamics simulation''' — Computational simulation of the physical movements of atoms and molecules over time; AI accelerates this dramatically.
* '''Drug discovery''' — The process of finding new therapeutic molecules; AI accelerates target identification, molecular design, property prediction, and clinical trial optimization.
* '''Materials informatics''' — Applying machine learning to accelerate the discovery of new materials with desired properties.
* '''Neural ODE''' — A neural network that parameterizes the derivative of a system's state, enabling learning of continuous dynamical systems.
* '''Physics-informed neural network (PINN)''' — A neural network trained to obey physical laws (e.g., differential equations) in addition to fitting data.
* '''Foundation model for science''' — A large pre-trained model fine-tuned for scientific tasks (e.g., ESM-2 for protein sequences, ChemBERTa for molecules).
* '''SMILES (Simplified Molecular Input Line Entry System)''' — A notation that encodes molecular structure as a text string, enabling LLMs to work with molecular data.
* '''Climate modeling''' — Simulating Earth's climate system to predict future states and understand climate dynamics; increasingly assisted by AI emulators.
* '''Generative chemistry''' — Using generative models (VAEs, GNNs, diffusion models) to design novel molecules with desired properties.
* '''Virtual screening''' — Using computational methods to rapidly screen large libraries of molecules for drug candidates.
* '''Active learning''' — A machine learning paradigm where the model queries a human or oracle for labels on the most informative examples — critical for expensive scientific experiments.
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