Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Sarcomas
are a group of malignant neoplasms of connective tissue
with a different etiology than carcinomas. The efforts to discover
new drugs with antisarcoma activity have generated large datasets
of multiple preclinical assays with different experimental conditions.
For instance, the ChEMBL database contains outcomes of 37,919 different
antisarcoma assays with 34,955 different chemical compounds. Furthermore,
the experimental conditions reported in this dataset include 157 types
of biological activity parameters, 36 drug targets, 43 cell lines,
and 17 assay organisms. Considering this information, we propose combining
perturbation theory (PT) principles with machine learning (ML) to
develop a PTML model to predict antisarcoma compounds. PTML models
use one function of reference that measures the probability of a drug
being active under certain conditions (protein, cell line, organism, etc.). 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.