  Most evolutionary-oriented deep generative models do not explicitly consider
the underlying evolutionary dynamics of biological sequences as it is performed
within the Bayesian phylogenetic inference framework. In this study, we propose
a method for a deep variational Bayesian generative model (EvoVGM) that jointly
approximates the true posterior of local evolutionary parameters and generates
sequence alignments. Moreover, it is instantiated and tuned for continuous-time
Markov chain substitution models such as JC69, K80 and GTR. We train the model
via a low-variance stochastic estimator and a gradient ascent algorithm. Here,
we analyze the consistency and effectiveness of EvoVGM on synthetic sequence
alignments simulated with several evolutionary scenarios and different sizes.
Finally, we highlight the robustness of a fine-tuned EvoVGM model using a
sequence alignment of gene S of coronaviruses.
