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

Kleandrova, Valeria V.; Scotti, Marcus Tullius; Speck‐Planche, Alejandro

doi:10.3390/antibiotics10081005

Cited by 19 publications

(20 citation statements)

References 61 publications

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“…Additionally, Table 3 contains two means calculated (using only the training set) for each of the D [ GTI ] cj descriptors: one considering only active cases/molecules and the other based on the inactive ones [ 73 , 75 , 76 ]. The tendency of variation reported in Table 3 gives a fast insight regarding how the increase or decrease of the value of each D [ GTI ] cj descriptor can improve the probability of a molecule to be active against both caspase-1 and TNF-alpha.…”

Section: Resultsmentioning

confidence: 99%

“…When searching for dual-target inhibitors which is the main purpose of this work, we used a series of metrics recently reported by Speck-Planche and coworkers. Such metrics were FA(%) and S(TSAD) [ 76 ]. For a query molecule, FA(%) measured the frequency (percentage of times) in which a molecule was predicted as active by considering the eight experimental conditions cj reported in this work (see Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

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In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

2021

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Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

2021

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“…All the steps necessary for the creation of a PTML-MLP model have been described in detail very recently [ 37 , 40 ]. Therefore, we will focus on the specific aspects of the two PTML-MLP models reported here.…”

Section: Methodsmentioning

confidence: 99%

“…To consider both the structure of any case/chemical and the experimental condition, cj, under which that case/chemical was tested, we applied a two-step approach, known as Box–Jenkins, which is the key aspect accounting for the great success of the PTML models [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 57 , 58 , 59 , 60 , 61 , 62 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…The experimental evidence explained above suggests that the simultaneous inhibition of caspase-1, TNF-alpha, and IGF1R by a multi-target agent could constitute a promising alternative against future PANC treatment. In this sense, finding such multi-target agents can be accelerated utilizing the methodology known as perturbation theory and machine learning (PTML), which allows the integration of different kinds of chemical and biological data [ 19 , 20 , 21 , 22 , 23 ] and has been successfully applied to different drug discovery areas, such as oncology [ 24 , 25 , 26 ], neuroscience [ 27 , 28 , 29 , 30 ], immunology and immunotoxicity [ 31 , 32 ], infectious diseases [ 33 , 34 , 35 , 36 , 37 , 38 ], and drug delivery [ 39 ]. Considering all the ideas mentioned until now, in this work we establish the theoretical foundations for the rational discovery of multi-target chemicals against caspase-1, TNF-alpha, and IGF1R.…”

Section: Introductionmentioning

confidence: 99%

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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Kleandrova

Speck‐Planche

2022

Biomedicines

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Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.

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Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

García-González

Marrero-Ponce

Brizuela

et al. 2023

Molecular Informatics

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Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non‐DL based approaches for small‐ and medium‐sized chemical datasets have demonstrated to be most suitable for. In this approach, an initial universe of molecular descriptors (MDs) is first calculated, then different feature selection algorithms are applied, and finally, one or several predictive models are built. Herein we demonstrate that this traditional approach may miss relevant information by assuming that the initial universe of MDs codifies all relevant aspects for the respective learning task. We argue that this limitation is mainly because of the constrained intervals of the parameters used in the algorithms that compute MDs, parameters that define the Descriptor Configuration Space (DCS). We propose to relax these constraints in an open CDS approach, so that a larger universe of MDs can be initially considered. We model the generation of MDs as a multicriteria optimization problem and tackle it with a variant of the standard genetic algorithm. As a novel component, the fitness function is computed by aggregating four criteria via the Choquet integral. Experimental results show that the proposed approach generates a meaningful DCS by improving state‐of‐the‐art approaches in most of the benchmarking chemical datasets accounted for.

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

Cited by 19 publications

References 61 publications

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

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