Machine Learning Study of Metabolic Networks<i>vs</i>ChEMBL Data of Antibacterial Compounds
Karel Diéguez‐Santana, Gerardo M. Casañola‐Martín, Roldán Torres, Bakhtiyor Rasulev, James R. Green, Humberto González‐Díaz
Abstract
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.
Topics & Concepts
chEMBLLinear discriminant analysisAntibacterial activityArtificial intelligenceRandom forestMachine learningAntibioticsAntibiotic resistancePseudo amino acid compositionBacteriaChemistryMathematicsComputational biologyAlgorithmComputer scienceBiologyDrug discoveryBiochemistryGeneticsDipeptideAmino acidComputational Drug Discovery MethodsProtein Structure and DynamicsMicrobial Metabolic Engineering and Bioproduction