Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.

For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.

Genetic basis and spatial distribution of glucose-6-phosphate dehydrogenase deficiency in ecuadorian ethnic groups: a malaria perspective.

BACKGROUND: Glucose-6-phosphate dehydrogenase deficiency (G6PDd) is an X-linked disorder affecting over 400 million people worldwide. Individuals with molecular variants associated with reduced enzymatic activity are susceptible to oxidative stress in red blood cells, thereby increasing the risk of pathophysiological conditions and toxicity to anti-malarial treatments. Globally, the prevalence of G6PDd varies among populations. Accordingly, this study aims to characterize G6PDd distribution within the Ecuadorian population and to describe the spatial distribution of reported malaria cases. METHODS: Molecular variants associated with G6PDd were genotyped in 581 individuals from Afro-Ecuadorian, Indigenous, Mestizo, and Montubio ethnic groups. Additionally, spatial analysis was conducted to identify significant malaria clusters with high incidence rates across Ecuador, using data collected from 2010 to 2021. RESULTS: The A- c.202G > A and A- c.968T > C variants underpin the genetic basis of G6PDd in the studied population. The overall prevalence of G6PDd was 4.6% in the entire population. However, this frequency increased to 19.2% among Afro-Ecuadorian people. Spatial analysis revealed 12 malaria clusters, primarily located in the north of the country and its Amazon region, with relative risks of infection of 2.02 to 87.88. CONCLUSIONS: The findings of this study hold significant implications for public health interventions, treatment strategies, and targeted efforts to mitigate the burden of malaria in Ecuador. The high prevalence of G6PDd among Afro-Ecuadorian groups in the northern endemic areas necessitates the development of comprehensive malaria eradication strategies tailored to this geographical region.

Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.

CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring.

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a data set including ligands and decoys for 102 targets that have been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. Our methodology showed to be outstandingly predictive when challenged to rescore external (previously unseen) data. Remarkably, CompScore was able not only to retain its performance after redocking with a different software, but also proved that the enrichment obtained was not artificial. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/ .