One-Liner

Novelty

Background

How do LLMs do graphs?

• predict text from graphs (convert graph into text, autoregression)
• align text with graph (GNN + LLM late fusion)
• encode text with graph (stick LLM embedding to a GNN as a prompt)

Motivation

Notable Methods

Key Figs

New Concepts

Notes

One-Liner

Novelty

Notable Methods

Key Figs

New Concepts

Notes

One-Liner

Notable Methods

1. construct a series of perturbation actions
   • \(A\qty(s)\) = decomposition (skip), expansion (rollout), dredirection
2. sequence actions with MCTS

Key Figs

New Concepts

Notes

Motivation: drug discovery is extremely slow and expensive; mostly modulating previous iterations of work.

Principles of Drug Discovery

• observation: acquire/fuse knowledge from multiple data modalities (sequence, stricture, etc.)
• think: critically generating actually new hypothesis — allowing iteratively
• allowing LMs to code-switch between moladities (i.e. fuse different modalities together in the most uniform way)

LM as a heuristic helps prune down search space quickly.

• matrix calculus
• supervised learning
• gradient descent
• Newton’s Method
• regression
  • \(y|x,\theta\) is linear Linear Regression
    • least-squares error: probabilistic intuition for least-squares error in linear regression
    • Normal Equation
  • What if \(X\) and \(y\) are not linearly related? Generalized Linear Model
    • \(y|x, \theta\) can be any distribution that’s exponential family
    • some exponential family distributinos: SU-CS229 Distribution Sheet
• classification
  • take linear regression, squish it: logistic regression; for multi-class, use softmax \(p\qty(y=k|x) = \frac{\exp \theta_{k}^{T} x}{\sum_{j}^{} \exp \theta_{j}^{T} x}\)
  • generative learning
    • modeling each class’ distributions, and then check which one is more likely: GDA
    • Naive Bayes
• bias variance tradeoff
• regularization
• unsupervised learning
  • k-means clustering
  • Gaussian mixture model and expectation maximization
    • Jensen’s Inequality
• feature map and precomputing Kernel Trick
• Decision Tree
• boosting