Bayesian analysis and inference from QSAR predictive model results

3D-QSAR and ADMET studies of morpholino-pyrimidine inhibitors of DprE1 from Mycobacterium tuberculosis

DprE1 is involved in the synthesis of Mycobacterium tuberculosis cell wall and is a potent drug target for Tuberculosis (TB) treatment. The structure and dynamics of the loops L-I and L-II flanking the inhibitor binding site was studied using molecular dynamics (MD) simulation and MMPBSA in Amber v18. Docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) of 55 Morpholino-pyrimidine (MP) inhibitors was carried out using Autodock v1.2.0 and Forge v10. ADMET analysis was done using SwissADME and pkCSM. All MP inhibitors docked in the DprE1 binding pocket, making contacts with L-II residues. MD studies showed that L-I and L-II unfold in the absence of the inhibitor but fold stably structure with reduced protein motions in the presence of MP-38, the highest affinity inhibitor. This was confirmed by k-means clustering and secondary structure analysis. L-II residues, L317, F320 and R325 contributed most towards the MMPBSA binding free energy of MP-38. A robust field-based 3D-QSAR model showed values of r2train = 0.982, r2test = 0.702 and q2 = 0.516. The MP inhibitor field points were broadly divided into negative electrostatics near the A, B rings and hydrophobic electrostatics near the D, E rings. Addition of negative groups at methanone position and ring B as well as addition of hydrophobic and bulky groups at ring E will improve activity. Highly active compounds 47, 49 and 50 of MP series exhibited highly favourable drug-like properties. SAR and ADMET insights attained from this model will help in the development of active DprE1 inhibitors in future.Communicated by Ramaswamy H. Sarma.

Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: A novel hope in anti-diabetes drug discovery

Conventional anti-diabetes agents exhibit some undesirable side effects. Recently, lactic acidosis and/or bladder cancer were also reported with the use of these agents. Hence, there is an urgent need for alternative anti-diabetes in order to reduce/avoid the unwanted effects. In this scenario sodium glucose cotransporter 2 (SGLT2) inhibitors has already been established as an important class of anti-diabetic drug. The search for new generation SGLT2 inhibitors with high affinity is still an ongoing process. Here, we aim to develop computational models to predict the SGLT2 inhibitory activity of small molecules based on chemical structures. This work provides in-silico analysis to propose possible fragment/fingerprint identification recommended for SGLT2 inhibitors. Up-to-our knowledge, this study is an initiative to propose fingerprints responsible for SGLT2 inhibition. Furthermore, we used nine different algorithms to build machine learning (ML) models that could be used to prioritize compounds as SGLT2 inhibitors from large libraries. The best performing ML models were applied to virtually screen a large collection of FDA approved drugs. The best predicted compounds have been recommended to be biologically investigated in future in order to identify next generation SGLT2 inhibitors with different chemical structure.

Uncertainty reduction in environmental data with conflicting information

The assessment of ecotoxicological effects of chemicals for regulatory purposes requires large amounts of experimental data which are expensive to obtain and eventually might entail exhaustive animal testing. The required decision-making processes in this regulatory context, must often be carried out with limited or even contradictory sources of information. To benefit from all sources of information without compromising the quality of the decision process, uncertainty management and reduction techniques, such as the Dempster-Shafer theory of evidence, have to be applied. This theory was applied to both experimental and in silico biodegradation data sources to assess chemical persistence. Uncertainties of the initially lass uncertain estimates for biodegradation rates in water were reduced by as much as 20-60%. The analysis showed that conflicting evidence can be detected, quantified, and redistributed proportionally among all the feasible subsets of hypotheses. The advantages of the Dempster-Shafer theory over Bayesian approaches to represent evidence concerning hypotheses by assigning probabilities were also analyzed.

Database composition can affect the structure-activity relationship prediction

The percent active (A) and inactive (I) chemicals in a database can directly affect the sensitivity (% active chemicals predicted correctly) and specificity (% inactive chemicals predicted correctly) of structure-activity relationship (SAR) analyses. Subdividing the National Center for Toxicological Research (NCTR) liver cancer database (NCTRlcdb) into various A/I ratios, which varied from 0.2 to 5.5, resulted in sensitivity/specificity ratios that varied from 0.1 to 6.5. As percent active chemicals increased (increasing A/I ratio), the sensitivity rose, the specificity decreased, and the concordance (% total chemicals predicted correctly) remained fairly constant. The numbers of chemicals in the various data sets ranged from 187 to 999 and appeared to have no affect on any of the 3 predictors of sensitivity, specificity, or concordance.

Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines

Statistical learning methods have been used in developing filters for predicting inhibitors of two P450 isoenzymes, CYP3A4 and CYP2D6. This work explores the use of different statistical learning methods for predicting inhibitors of these enzymes and an additional P450 enzyme, CYP2C9, and the substrates of the three P450 isoenzymes. Two consensus support vector machine (CSVM) methods, "positive majority" (PM-CSVM) and "positive probability" (PP-CSVM), were used in this work. These methods were first tested for the prediction of inhibitors of CYP3A4 and CYP2D6 by using a significantly higher number of inhibitors and noninhibitors than that used in earlier studies. They were then applied to the prediction of inhibitors of CYP2C9 and substrates of the three enzymes. Both methods predict inhibitors of CYP3A4 and CYP2D6 at a similar level of accuracy as those of earlier studies. For classification of inhibitors of CYP2C9, the best CSVM method gives an accuracy of 88.9% for inhibitors and 96.3% for noninhibitors. The accuracies for classification of substrates and nonsubstrates of CYP3A4, CYP2D6, and CYP2C9 are 98.2 and 90.9%, 96.6 and 94.4%, and 85.7 and 98.8%, respectively. Both CSVM methods are potentially useful as filters for predicting inhibitors and substrates of P450 isoenzymes. These methods generally give better accuracies than single SVM classification systems, and the performance of the PP-CSVM method is slightly better than that of the PM-CSVM method.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.