Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets

Orosz, Álmos; Héberger, Károly; Rácz, Anita

doi:10.3389/fchem.2022.852893

Cited by 37 publications

(24 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model with the combined use of molecular descriptors can outperform the analytical-descriptorbased model, as seen in previous studies [7,30]; their individual use yields a performance comparable to that of the analytical-descriptor-based model in terms of log Mw (except with the topological descriptor), BP, MP, and log VP.…”

Section: Model Performance Using Analytical and Molecular Descriptorssupporting

confidence: 59%

Mining structural information in gas chromatography-mass spectrometry data for analytical-descriptor-based quantitative structure–activity relationship

Zushi

2023

Preprint

View full text Add to dashboard Cite

Recently, a new approach to quantitative structure–activity relationship (QSAR) has been proposed, which employs machine learning techniques and uses analytical signals from the full scan of mass spectra as input. Unlike traditional QSAR, this approach does not need exhaustive structural determination to assess numerous unknown compounds. The new approach assumes that a mass spectral pattern reflects the structure of a target chemical. However, despite the remarkable performance of this method, the relationship between the spectrum and the structure is complex and its interpretation is a challenge to the further development of QSAR based on analytical signals. This study explored whether gas chromatography-mass spectrometry (GC-MS) data contain meaningful structural information that is advantageous for QSAR prediction by comparing it with the traditional molecular descriptor used in QSAR prediction. Chemical groups were assigned to each chemical linked to the GC-MS data and molecular descriptor dataset to investigate their relationships. Then, data clustering was performed by t-distributed stochastic neighbor embedding on the GC-MS data (i.e., analytical descriptor) and on four molecular descriptors: ECFP6, topological descriptor in CDK, MACCS key, and PubChem fingerprint. Although the chemicals represented by the analytical descriptor were not clearly clustered according to the chemical class, most clusters were formed by chemicals with similar spectrum patterns. An additional investigation suggested that the analytical and molecular descriptors preserved structural information in different ways. The predictive performance of QSAR based on analytical and molecular descriptors was evaluated in terms of molecular weight, log Ko−w, boiling point, melting point, vapor pressure, water solubility, and two oral toxicities in rats and mice. The analytical- and molecular-descriptor-based models performed comparably. The influential variables in the analytical-descriptor-based model were further investigated by comparing analytical-descriptor-based and linear regression models using simple indicators of the mass spectrum. In general, the analytical-descriptor-based approach predicted the physicochemical properties and toxicities of structurally unknown chemicals that the molecular-descriptor-based one could not. These results suggest that the new approach is valuable for evaluating unknown chemicals in many scenarios.

show abstract

Section: Model Performance Using Analytical and Molecular Descriptorssupporting

confidence: 59%

Mining structural information in gas chromatography-mass spectrometry data for analytical-descriptor-based quantitative structure–activity relationship

Zushi

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Molecules are characterized through structural invariants, that is, by descriptors that are independent of molecular conformation. Many of them are topological indices (TI) [81][82][83][84][85][86][87], which are capable of characterizing most of the molecular structure [88][89][90][91][92][93][94][95].…”

Section: Methodsmentioning

confidence: 99%

Similarity-based Virtual Screening to Find Antituberculosis Agents based on Novel Scaffolds

García-García¹,

Julián-Ortiz²,

Gálvez³

et al. 2022

Preprint

View full text Add to dashboard Cite

A method is developed to identify molecular scaffolds potentially active against the Mycobacterium tuberculosis complex (MTBC). A structurally heterogeneous set of compounds active against MTBC was used to obtain a structural pattern model based on structural invariants. This model was statistically validated through a Leave-n-Out test. It successfully discriminated between active or inactive compounds over 86% in database sets and was also able to select new active chemical structures in external databases. The selection of new substituted pyrimidines, pyrimidones and triazolo[1,5-a]pyrimidines was particularly interesting because these structures could provide new scaffolds in this field. The seven selected candidates were synthesized and six of them showed activity in vitro.

show abstract

“…QSAR models require large and diverse data sets to ensure that the models can capture the variability and complexity of the toxicity outcomes. However, the availability of high-quality toxicity data is often limited, and the data sets used to build QSAR models may be biased or incomplete. , Then the selection of descriptors is a critical step in QSAR analysis as it determines the accuracy, reliability, and robustness of the model, and there are limitations to using chemical descriptors for toxicity prediction . Chemical descriptors capture only the structural properties of a chemical and do not consider other factors, such as metabolism, bioavailability, and toxicokinetics.…”

Section: Qsar and Its Limitationsmentioning

confidence: 99%

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

View full text Add to dashboard Cite

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

show abstract

Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets

Cited by 37 publications

References 37 publications

Mining structural information in gas chromatography-mass spectrometry data for analytical-descriptor-based quantitative structure–activity relationship

Mining structural information in gas chromatography-mass spectrometry data for analytical-descriptor-based quantitative structure–activity relationship

Similarity-based Virtual Screening to Find Antituberculosis Agents based on Novel Scaffolds

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Contact Info

Product

Resources

About