AI for Science — Overview
Most of this site covers general-purpose ML/AI infrastructure and techniques. This section is different: it's about where those same techniques (graph neural networks, generative models, transformers) get applied directly to scientific discovery itself — predicting a protein's 3D structure, generating a candidate drug molecule, simulating physical systems — genuinely accelerating fields that used to depend entirely on slow, expensive physical experimentation.
What's in this section
- AI for Science Fundamentals — drug discovery and molecular ML, protein structure prediction, genomics, materials science, physics-informed neural networks and neural operators, molecular generation and docking, and scientific foundation models.
Why This Belongs on an AI Engineering Site
Every technique here is a direct, recognizable application of methods covered elsewhere on this site — Graph ML for molecules, Generative Models for candidate molecule design, Attention & Transformers for protein sequences and scientific foundation models. The domain expertise (chemistry, biology, physics) is what's genuinely new — the ML machinery is the same toolkit the rest of this site builds, aimed at a different, high-stakes target.
See the roadmap for the full path.