QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

Abstract

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

“…At the same time, this cut-off prevents excessive imbalance between active and inactive compounds. The software QUBILs-MAS v1.0 [30] was employed to calculate the molecular descriptors known as the atom-based quadratic indices, which have been earlier proved to be highly efficient for developing mt-QSAR models [27,31,32,33,34,35]. A detailed description of how these descriptors are calculated is provided in the Materials and Methods section.…”

Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors

“…Then, using the sdf file as input, we employed the software QuBiLS-MAS v1.0 to compute the molecular descriptors known as local atom-based stochastic quadratic indices LQI [ 51 , 52 ]. These are topological descriptors with successful applications in medicinal chemistry and drug discovery [ 53 , 54 ]. We calculated the LQI descriptors of order k (with k from 0 to 5) by using predefined parameters such as algebraic form (quadratic), constraints (atom-based), matrix type (stochastic), cutoff (keep all), groups (local—referring to specific atom types such as aliphatic and aromatic carbons, methyl groups, halogens, and heteroatoms), and aggregation operator (Manhattan distance).…”

Use of big data in drug development for precision medicine: an update

“…QSAR datasets often involve a large number of compounds (>100,000) and descriptors (>1,000), and therefore, prioritizing drug compounds from QSAR is often computationally intensive and requires the adjustment of many sensitive parameters to achieve good prediction [94]. To address these challenges, various machine learning methods have been applied to QSAR, such as linear discriminant analysis [95], k nearest neighbors [96], decision tree [97], support vector machine [98] and random forest [99]. In particular, random forest has been very popular since it was introduced as a QSAR method [99].…”