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- Carlile Lavor, Leo Liberti, Nelson Maculan, Antonio Mucherino
- Comp. Opt. and Appl.
- 2012

Given a simple weighted undirected graph G = 3 such that ||xu − xv|| = duv for each {u, v} ∈ E. We show that under a few assumptions usually satisfied in proteins, the MDGP can be formulated as a search in a discrete space. We call this MDGP subclass the Discretizable MDGP (DMDGP). We show that the DMDGP is NP-hard and we propose a solution algorithm called… (More)

- Antonio Mucherino, Leo Liberti, Carlile Lavor, Nelson Maculan
- GECCO
- 2009

We consider the Discretizable Molecular Distance Geometry Problem (DMDGP), which consists in a subclass of instances of the distance geometry problem related to molecular conformations for which a combinatorial reformulation can be supplied. We investigate the performances of two different algorithms for solving the DMDGP. The first one is the Branch and… (More)

- Carlile Lavor, Antonio Mucherino, Leo Liberti, Nelson Maculan
- 2009 International Multiconference on Computer…
- 2009

NMR experiments are able to provide some of the distances between pairs of hydrogen atoms in molecular conformations. The problem of finding the coordinates of such atoms is known as the molecular distance geometry problem. This problem can be reformulated as a combinatorial optimization problem and efficiently solved by an exact algorithm. To this purpose,… (More)

- Antonio Mucherino, Petraq Papajorgji, Panos M. Pardalos
- Operational Research
- 2009

- Antonio Mucherino
- GSI
- 2013

- Antonio Mucherino, Susan Costantini, Daniela di Serafino, Marco D'Apuzzo, Angelo M. Facchiano, Giovanni Colonna
- Computational Biology and Chemistry
- 2008

Recent studies suggest that protein folding should be revisited as the emergent property of a complex system and that the nature allows only a very limited number of folds that seem to be strongly influenced by geometrical properties. In this work we explore the principles underlying this new view and show how helical protein conformations can be obtained… (More)

- Leo Liberti, Carlile Lavor, Antonio Mucherino, Nelson Maculan
- ITOR
- 2011

Distance geometry problems arise from the need to position entities in the Euclidean K-space given some of their respective distances. Entities may be atoms (molecular distance geometry), wireless sensors (sensor network localization), or abstract vertices of a graph (graph drawing). In the context of molecular distance geometry, the distances are usually… (More)

- Leo Liberti, Carlile Lavor, Nelson Maculan, Antonio Mucherino
- SIAM Review
- 2014

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consist of an incomplete set of distances and the output is a set of points in Euclidean space realizing those given distances. We survey the theory of Euclidean distance geometry and its most important… (More)

- Carlile Lavor, Jon Lee, Audrey Lee-St. John, Leo Liberti, Antonio Mucherino, Maxim Sviridenko
- Optimization Letters
- 2012

Given a weighted, undirected simple graph G = (V, E, d) (where d : E → R +), the Distance Geometry Problem (DGP) is to determine an embedding x : V → R K such that ∀{i, j} ∈ E x i − x j = d ij. Although, in general, the DGP is solved using continuous methods, under certain conditions the search is reduced to a discrete set of points. We give one such… (More)

—The Discretizable Molecular Distance Geometry Problem (DMDGP) consists in a subclass of distance geometry instances (related to molecules) that can be solved by combinatorial optimization. A modified version of the Branch and Prune (BP) algorithm, previously proposed for solving these instances, is presented, where it is supposed that exact distances are… (More)