Machine learning-aided algorithm for predicting spectral properties and cytotoxicity of BODIPY-appended platinum complexes
Source abstract
Abstract
We present the results of creating models based on machine learning methods aimed at accurately predicting absorption maximum wavelength and half maximal inhibitory concentration of BODIPY-appended platinum complexes (Pt-BODIPYs). For this purpose, we compiled a unique database on spectral characteristics and cytotoxicity parameters of Pt-BODIPYs, comprising 447 records. Using this database and applying the CatBoost/ECFP and kNN/text vectorization algorithms, while considering various experimental conditions (solvent, cell line, presence or absence of light), we successfully trained models to forecast the absorption maximum wavelength (MAE ∼ 14 nm) and half maximal inhibitory concentration (MAE ∼ 0.26 log unit) for Pt-BODIPYs. For enhanced accessibility, we have made these trained models available on our web service ChemPredictor at http://chem-predictor.isc-ras.ru/individual/abs_log/ .
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Luminescence and Fluorescent Materials · Molecular Sensors and Ion Detection · Nanoplatforms for cancer theranostics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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