Comparative study to predict toxic modes of action of phenols from molecular structures

Abstract

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set

“…After performing a representative selection of TS and PS , LDA was used to fit discriminant functions that permit the classification of compounds as either BBB+ or BBB− using a cut‐off value of 0.0 for the brain exposure classification. The LDA has become an important tool successfully applied in the field of BBB as well as others areas of drug design and property estimation 67–69. In this sense, its application in the context of BBB passage prediction when it is not always necessary to predict an exact value, understand the probability that a compound will have passage to the brain or not can be very helpful.…”

“…The LDA has become an important tool successfully applied in the field of BBB as well as others areas of drug design and property estimation. [67][68][69] In this sense, its application in the context of BBB passage prediction when it is not always necessary to predict an exact value, understand the probability that ac ompound will have passage to the brain or not can be very helpful.…”

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

“…7a shows the snapshot of the overall superimposition of compound no. 47 and the virtually designed compounds (54)(55)(56)(57)(58)(59). 7b shows the a The compounds whose activity is reported in approximate terms.…”

“…The four template groups incorporated in the present work are steric (S ALL ), hydrogen donor (HD ALL ), hydrogen acceptor (HA ALL ) and ring (R ALL ). [58][59][60][61] The development of these rational SAR models focus on necessary chemical features leading to a better pharmaco-toxico-kinetic prole of a lead candidate curtailing irrelevant experimental determinations. 55 Optimization of physicochemical properties, such as lipophilicity and increase ligand-lipophilicity efficiency (LLE) is reported by Mowbray et al 56 These descriptors are selected to characterize the molecular architecture for their capability to correlate diverse biochemical phenomena of the concerned molecular series.…”

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

“…In another study, QSAR models were developed to predict the toxicity of 221 phenols in the ciliated protozoan Tetrahymena pyriformis [154]. ML methods such as k-NN, SVM and classification trees were used to classify the molecules in one of the four modes of toxic action: polar narcotics, weak acid respiratory uncouplers, proelectrophiles and soft electrophiles.…”