An NLP-based technique to extract meaningful features from drug SMILES

Sharma, Rahul; Saghapour, Ehsan; Chen, Jake Y.

doi:10.1016/j.isci.2024.109127

Cited by 2 publications

References 34 publications

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Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Tariq,

Ahrens,

Alygizakis

et al. 2024

Toxics

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Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

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Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Tariq,

Ahrens,

Alygizakis

et al. 2024

Toxics

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Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

Ajakwe,

Deji-Oloruntoba,

Olatunbosun

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Reliable data analysis depends on effective data preparation, especially since AI-driven business intelligence depends on unbiased and error-free data for decision-making. However, developing a reliable dataset is a difficult task that requires expertise. Due to the costly damage a negligible error in data can cause to a system, a good understanding of the processes of quality data transformation is necessary. Data varies in properties, which determines how it is generated, the errors in it, and the transformations it needs to undergo before it is fed into a model. Also, most data used for analytics is sourced from public stores without means to verify its quality or what further steps need to be taken in preprocessing it for optimal performance. This chapter provides a detailed description of practical and scientific procedures to generate and develop quality data for different models and scenarios. Also, it highlights the tools and techniques to clean and prepare data for optimal performance and prevent unreliable data analytics outcomes.

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An NLP-based technique to extract meaningful features from drug SMILES

Cited by 2 publications

References 34 publications

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

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