RESUMO

Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment.

Assuntos

Antivirais , Aprendizado Profundo , Reposicionamento de Medicamentos , Monkeypox virus , Antivirais/farmacologia , Monkeypox virus/efeitos dos fármacos , Reposicionamento de Medicamentos/métodos , Pirazinas/farmacologia , Simulação de Acoplamento Molecular , Dibenzotiepinas , Amidas/farmacologia , Ribavirina/farmacologia , Triazinas/farmacologia , Mpox/tratamento farmacológico , Mpox/virologia , Humanos , Aprendizado de Máquina , Morfolinas , Piridonas

RESUMO

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a new criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer (S2D) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present our proposed hyper-heuristic that exploits the S2D and one of the internal properties of the given strings to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show that our proposed dichotomizer (i.e., S2D) can classify datasets with 98% of accuracy. Also, our proposed hyper-heuristic obtains competitive performance in comparison with the best methods and outperforms best hyper-heuristics for uncorrelated datasets in terms of both quality of solutions and run time factors. All supplementary files, including the source codes and datasets, are publicly available on GitHub.1.