1. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds
- Author
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Karel Diéguez-Santana, Gerardo M. Casañola-Martin, Roldan Torres, Bakhtiyor Rasulev, James R. Green, Humbert González-Díaz, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), and Eusko Jaurlaritza
- Subjects
ChEMBL ,Machine learning ,Drug Discovery ,Complex networks ,Pharmaceutical Science ,Molecular Medicine ,Multidrug-resistant ,antibacterial compounds ,Perturbation theory ,Information fusion ,Antibacterial compounds - 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., G.D.H. acknowledges financial support from grants from the Ministry of Science and Innovation (PID 2019-104148 GB-I00) and grant no. IT1045-16-2016–2021 from the Basque Government.
- Published
- 2022