Databases for the study of biofilms: current status and potential applications

Martins, Fábio G.; Melo, André; Sousa, Sérgio F.

doi:10.1080/08927014.2021.1876849

Cited by 3 publications

(1 citation statement)

References 86 publications

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“…The applications of ML in drug discovery are multivariate, both integrated with complementary computer-aided drug discovery techniques such as molecular docking [31][32][33] and virtual screening [34][35][36] or being used on their own [37][38][39][40]. Furthermore, the growing availability of biological and molecular data in online databases [41] allows the development of more specific and successful models. ML models can be divided into supervised learning (SL), where the training data include both the input and the target or desired results, unsupervised learning (UL), mostly used for clustering purposes, and semisupervised models, which use labelled and unlabelled samples to improve the performance of the model [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa

Carneiro

Magalhães

Roque

et al. 2023

J Comput Aided Mol Des

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Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

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