Assessment of computational approaches in the prediction of spectrogram and chromatogram behaviours of analytes in pharmaceutical analysis: assessment review

Abstract

Abstract Background Today, artificial intelligence-based computational approach is facilitating multitasking and interdisciplinary analytical research. For example, the data gathered during an analytical research project such as spectral and chromatographic data can be used in predictive experimental research. The spectral and chromatographic information plays crucial role in pharmaceutical research, especially use of instrumental analytical approaches and it consume time, man power, and money. Hence, predictive analysis would be beneficial especially in resource-limited settings. Main body Computational approaches verify data at an early phase of study in research process. Several in silico techniques for predicting analyte’s spectral and chromatographic characteristics have recently been developed. Understanding of these tools may help researchers to accelerate their research with boosted confidence and prevent researchers from being misled by incorrect analytical data. In this communication, the properties of chemical compounds and its relation to chromatographic retention will be discussed, as well as the prediction technique for UV/IR/Raman/NMR spectrograms. This review looked at the reference data of chemical compounds to compare the predictive ability in silico tools along with the percentage error, limitations, and advantages. Conclusion The computational prediction of analytical characteristics offers a wide range of applications in academic research, bioanalytical method development, computational chemistry, analytical method development, data analysis approaches, material characterization, and validation process.