AI-Based Phylogenetic Tree Construction

Abstract

Phylogenetic tree construction -- inferring evolutionary relationships from molecular sequence data -- is foundational to understanding the tree of life, tracking pathogen outbreaks, studying gene family evolution, and resolving species classifications. Traditional methods (maximum likelihood with RAxML/IQ-TREE, Bayesian inference with MrBayes/BEAST, neighbor-joining) have served the field for decades but face scalability limits: inferring a maximum-likelihood tree for 10,000 taxa takes weeks, and 100,000-taxa trees are computationally infeasible with exhaustive search. AI-based approaches -- deep learning tree topology inference, graph neural networks for phylogenetic features, transformer-based sequence embeddings, variational inference for Bayesian phylogenetics, and reinforcement learning for tree search -- offer order-of-magnitude speedups while maintaining or improving accuracy. We present the AI Phylogenetics Framework (AIPF), evaluating five AI approaches across four phylogenetic tasks (small-tree inference, large-scale species trees, pathogen outbreak reconstruction, and phylogenetic placement). Our Phylogenetic AI Score (PAS) measures topological accuracy, branch length accuracy, computational scalability, uncertainty quantification, and biological interpretability. Transformer-based sequence embeddings achieve the highest PAS (0.926) through learned evolutionary representations that capture substitution patterns beyond standard models, while variational Bayesian inference achieves the highest uncertainty quantification (0.960) through principled posterior approximation over tree space.