Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction

Abstract

BackgroundPoor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.ResultsThe models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.ConclusionThe logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

The LUX Score: A Metric for Lipidome Homology

“…There, the problem is sometimes easier, especially when one is dealing with derivatives which are closely related, but even in small molecule cheminformatics, there is no universally accepted scheme [ 29 ]. Optimization of such structures often depends on the interaction sites of a protein and pharmaceutical requirements for administration of drugs [ 30 , 31 ]. In this study, the analysis does not stop after comparing the details of individual structures.…”

The LUX Score: A Metric for Lipidome Homology

“…There, the problem is sometimes easier, especially when one is dealing with derivatives which are closely related, but even in small molecule chemoinformatics, there is no universally accepted scheme (Bender et al, 2009). Optimization of such structures often depends on the interaction sites of a protein and pharmaceutical requirements for administration of drugs (Mohanapriya and Achuthan, 2012;Ahmed and Ramakrishnan, 2012). In this study, the analysis does not stop after comparing the details of individual structures.…”

Network pharmacology based investigation into the bioactive ingredients and molecular mechanisms of QingFeiPaiDu Decoction treating COVID-19

“…Using Traditional Chinese Medicine Systems Pharmacology [4] (TCMSP, http://tcmspw.com/index.php) and Traditional Chinese Medicines Integrated Database [5] (TCMID, http://119.3.41.228:8000/tcmid/search), the active ingredients of QFPDD were screened with oral bioavailability (OB)≥30% and drug-likeness [6][7] (DL)≥0.18…”