Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds
Alejandro Cabrera‐Andrade, Andrés López‐Cortés, Cristian R. Munteanu, Alejandro Pazos, Yunierkis Pérez‐Castillo, Eduardo Tejera, Sonia Arrasate, Humberto González‐Díaz
Abstract
.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.
Topics & Concepts
chEMBLComputer scienceMachine learningArtificial intelligenceVirtual screeningLinear discriminant analysisArtificial neural networkBioinformaticsDrug discoveryBiologyComputational Drug Discovery MethodsMicrobial Natural Products and BiosynthesisBioinformatics and Genomic Networks