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Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds.
- Source :
-
Molecular pharmaceutics [Mol Pharm] 2022 Jul 04; Vol. 19 (7), pp. 2151-2163. Date of Electronic Publication: 2022 Jun 07. - Publication Year :
- 2022
-
Abstract
- Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
Details
- Language :
- English
- ISSN :
- 1543-8392
- Volume :
- 19
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Molecular pharmaceutics
- Publication Type :
- Academic Journal
- Accession number :
- 35671399
- Full Text :
- https://doi.org/10.1021/acs.molpharmaceut.2c00029