Validation strategies for target prediction methods

Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.publishedVersio

The Development of Bilingual Teaching Materials on Mathematical Logic Based on Integrated Mathematics

This research aims to develop bilingual teaching materials mathematical logic integrated with Islamic religious values, to more easily convey mathematical materials with everyday life. This development research uses 4D models (define, design, devlop, disseminate). The subject of this study is student of university nahdlatul ulama west nusa tenggara. The instruments used are satisfaction questionnaires and validation questionnaires. Validity includes expert media validation, linguist validation, and validation of the substance of mathematical material. Research results based on expert validation include aspects of the feasibility of teaching materials getting a score of 91.5% with very decent criteria, and aspects of the validity of teaching materials reaching 210 with “highly valid” criteria, aspects of practicality of teaching materials reaching 90%. Based on these results, the bilingual teaching material integrated mathematical logic developed practically and validly for use

Machine learning for target discovery in drug development.

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant 807281) and FCT/FEDER (02/SAICT/2017, Grant 28333) for funding. G.J.L.B. is a Royal Society University Research Fellow (URF\R\180019) and a FCT Investigator (IF/00624/2015)

Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit.

Background and purpose: Identifying the macromolecular targets of drug molecules is a fundamental aspect of drug discovery and pharmacology. Several drugs remain without known targets (orphan) despite large-scale in silico and in vitro target prediction efforts. Ligand-centric chemical-similarity-based methods for in silico target prediction have been found to be particularly powerful, but the question remains of whether they are able to discover targets for target-orphan drugs. Experimental Approach: We used one of these in silico methods to carry out a target prediction analysis for two orphan drugs: actarit and malotilate. The top target predicted for each drug was carbonic anhydrase II (CAII). Each drug was therefore quantitatively evaluated for CAII inhibition to validate these two prospective predictions. Key Results: Actarit showed in vitro concentration-dependent inhibition of CAII activity with submicromolar potency (IC50 = 422 nM) whilst no consistent inhibition was observed for malotilate. Among the other 25 targets predicted for actarit, RORγ (RAR-related orphan receptor-gamma) is promising in that it is strongly related to actarit's indication, rheumatoid arthritis (RA). Conclusion and Implications: This study is a proof-of-concept of the utility of MolTarPred for the fast and cost-effective identification of targets of orphan drugs. Furthermore, the mechanism of action of actarit as an anti-RA agent can now be re-examined from a CAII-inhibitor perspective, given existing relationships between this target and RA. Moreover, the confirmed CAII-actarit association supports investigating the repositioning of actarit on other CAII-linked indications (e.g., hypertension, epilepsy, migraine, anemia and bone, eye and cardiac disorders)

Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.publishedVersio

Novel drug-target interactions via link prediction and network embedding

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04650-w

Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation

[EN] This study aims to estimate the performance and losses of a 50 MW photovoltaic (PV) utility-scale after 12 years of operation. The PV plant has monocrystalline and polycrystalline silicon modules and is located in the central region of Spain with an annual insolation of 1976 kWh/m2. Monitoring data over the entire year 2020 has been analyzed and filtered to assess the performance results following the IEC 61724 standard guidelines. The annual average reference yield, final yield, performance ratio and capacity utilization factor are of 5.44 h/d, 4.28 h/d, 79.24%, and 19.77%, respectively. Besides the experimental analysis, this work improves the estimation of the daily performance ratio, especially in days with low insolation. Two different modelling approaches have been assessed and compared. In first place, a physical model has been adopted, based on the most common losses, and including an exponential expression to account for low irradiance losses. In second place, statistical models have been used, with either multiple linear regressions or random forest algorithms. In contrast with other published models which require many inputs, the best accuracy has been reached with the random forest model using only the ambient temperature and solar irradiance as predictors, obtaining a RMSE of 1% for the PR and for the energy production.The authors gratefully acknowledge the operation & maintenance staff of the PV Power Plant in Olmedilla de Alarcon ¿ for providing the measured data of the solar PV power plant.Fuster-Palop, E.; Vargas-Salgado, C.; Ferri-Revert, JC.; Payá-Herrero, J. (2022). Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation. Renewable and Sustainable Energy Reviews. 170:1-17. https://doi.org/10.1016/j.rser.2022.11296811717

Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role of in silico Techniques

Natural products and their derivatives are the most promising and prolific resources in identifying the therapeutic small compounds with potential therapeutic activity. Nowadays, working with herbal or natural products can be boosted by collecting the data available for their chemical, pharmacological, and biological characteristics properties. Using in silico tools and methods, we can enhance the chances of getting a better result in a precise way. It can support experiments to emphasis their sources in fruitful directions. Though due to their limitations with respect to current knowledge, quality, quantity, relevance of the present data as well as the scope and limitations of cheminformatics methods, herbal product-based drug discovery is limited. The pharmaceutical re-profiling is done with the main objective to establish strategies by using approved drugs and rejected drug candidates in the diagnosis of new diseases. Drug repurposing offers safety lower average processing cost for already approved, withdrawn drug candidates. In silico methods could be oppressed for discovering the actions of un-investigated phytochemicals by identification of their molecular targets using an incorporation of chemical informatics and bioinformatics along with systems biological approaches, hence advantageous for small-molecule drug identification. The methods like rule-based, similarity-based, shape-based, pharmacophore-based, and network-based approaches and docking and machine learning methods are discussed

Development, validation and application of in-silico methods to predict the macromolecular targets of small organic compounds

Computational methods to predict the macromolecular targets of small organic drugs and drug-like compounds play a key role in early drug discovery and drug repurposing efforts. These methods are developed by building predictive models that aim to learn the relationships between compounds and their targets in order to predict the bioactivity of the compounds. In this thesis, we analyzed the strategies used to validate target prediction approaches and how current strategies leave crucial questions about performance unanswered. Namely, how does an approach perform on a compound of interest, with its structural specificities, as opposed to the average query compound in the test data? We constructed and present new guidelines on validation strategies to address these short-comings. We then present the development and validation of two ligand-based target prediction approaches: a similarity-based approach and a binary relevance random forest (machine learning) based approach, which have a wide coverage of the target space. Importantly, we applied a new validation protocol to benchmark the performance of these approaches. The approaches were tested under three scenarios: a standard testing scenario with external data, a standard time-split scenario, and a close-to-real-world test scenario. We disaggregated the performance based on the distance of the testing data to the reference knowledge base, giving a more nuanced view of the performance of the approaches. We showed that, surprisingly, the similarity-based approach generally performed better than the machine learning based approach under all testing scenarios, while also having a target coverage which was twice as large. After validating two target prediction approaches, we present our work on a large-scale application of computational target prediction to curate optimized compound libraries. While screening large collections of compounds against biological targets is key to identifying new bioactivities, it is resource intensive and challenging. Small to medium-sized libraries, that have been optimized to have a higher chance of producing a true hit on an arbitrary target of interest are therefore valuable. We curated libraries of readily purchasable compounds by: i. utilizing property filters to ensure that the compounds have key physicochemical properties and are not overly reactive, ii. applying a similaritybased target prediction method, with a wide target scope, to predict the bioactivities of compounds, and iii. employing a genetic algorithm to select compounds for the library to maximize the biological diversity in the predicted bioactivities. These enriched small to medium-sized compound libraries provide valuable tool compounds to support early drug development and target identification efforts, and have been made available to the community. The distinctive contributions of this thesis include the development and benchmarking of two ligand-based target prediction approaches under novel validation scenarios, and the application of target prediction to enrich screening libraries with biologically diverse bioactive compounds. We hope that the insights presented in this thesis will help push data driven drug discovery forward.Doktorgradsavhandlin

Desarrollo de algoritmos de clasificación supervisada de bajo coste computacional para sistemas embebidos orientado a la optimización de recursos lógicos

Desarrollar algoritmos de clasificación supervisada de bajo coste computacional para Sistemas Embebidos orientado a la optimización de recursos lógicos.En el presente trabajo de titulación se realiza una implementación de algoritmos de clasificación supervisada para sistemas embebidos de bajos recursos computacionales, ya que estos sistemas se ven limitados en el procesamiento de datos cuando se usan algoritmos de aprendizaje de máquina robustos, ocasionando en el sistema un alto tiempo de respuesta, debido a que, en el procesamiento es en donde se consume la mayor cantidad de recursos. Para su desarrollo, se aplicó técnicas de optimización de código para simplificar los procesos y el uso de recursos lógicos. Además, los mencionados algoritmos fueron evaluados bajo criterios de tiempo de procesamiento, tasa de error, tamaño y rendimiento. En la parte final como resultados relevantes, se observan las pruebas de los algoritmos de clasificación con cada base de datos utilizada, como también la implementación de curvas ROC y AUC. Con ello, se realiza una comparación de los resultados para determinar la eficiencia lograda por cada algoritmo optimizado.Ingenierí