Zichao Yan

PhyloGFN: Phylogenetic Inference with Generative Flow Networks

Mingyang Zhou

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (voir plus)nd numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. Our code is available at https://github.com/zmy1116/phylogfn.

2024-01-15

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Alex Hernandez-Garcia

Cheng-Hao Liu

Santiago Miret

Pablo Lemos

Luca Thiede

Zichao Yan

Alán Aspuru-Guzik

Yoshua Bengio

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (voir plus)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

doi.org

openreview.net

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

Supplementary data are available at Bioinformatics online.

2022-04-13

Bioinformatics (publié)