# adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis

arXiv: https://arxiv.org/abs/2501.18456 · HTML: https://mlsanity.com/paper/2501.18456/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines](https://mlsanity.com/paper/2105.13889/) (2021)
- [Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families](https://mlsanity.com/paper/2011.11259/) (2020)

## Built on by (most cited first, 2 of 2)

- [Expanding functional protein sequence space using high entropy generative models](https://mlsanity.com/paper/2605.03578/) (2026)
- [Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering](https://mlsanity.com/paper/2607.27077/) (2026)

## Abstract (arXiv)

In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package textttadabmDCA 2.0 is available in different programming languages (C++, Julia, Python) usable on different architectures (single-core and multi-core CPU, GPU) using a common front-end interface. In addition to several learning protocols for dense and sparse generative DCA models, it allows to directly address common downstream tasks like residue-residue contact prediction, mutational-effect prediction, scoring of sequence libraries and generation of artificial sequences for sequence design. It is readily applicable to protein and RNA sequence data.