Large-scale prediction of biological activities with Active-IT system

Traditional testing methods in pharmaceutical development can be time-consuming and costly, but in silico evaluation tools can offer a solution. Our in-house Active-IT system, a Ligand-Based Virtual Screening (LBVS) tool, was developed to predict the biological and pharmacological activities of small organic molecules. It includes four independent modules for generating molecular descriptors (3D-Pharma), machine learning modeling (ExCVBA), a database of bioactivity models, and a prediction module. Activity data collected from the PubChem BioAssay database was used for modelling SVM and Naïve Bayes machine learning methods. Models have been constructed using a recursive stratified partition method and validated through an activity randomization (Y-random) process. Over 3500 bioassays were modeled, each comprising 30 SVM and 30 Naïve Bayes models and 60 randomized models. Bioassays with low performance or discrimination between regular and randomized were discarded. Using the Active-IT system we have evaluated three bioactive compounds of Ayahuasca tea. The predictions were thoroughly validated using known targets described in several public databases. The external validation results are noteworthy, with 16 of 33 (48.5% with p-value.