The first intention of our research was to handle the issue of transcriptomic knowledge complexity by introducing a novel Transcriptomic Response Index (TRI), compressing the whole transcriptomic area right into a single variable, and linking it with the inhaled Multiwalled Carbon Nanotubes (MWCNTs) properties. This technique permits us to foretell fold change values of hundreds of differentially expressed genes (DEGs) utilizing a single variable and a single Quantitative Construction-Exercise Relationship (QSAR) mannequin. Within the context of this work, TRI compressed 5167 DEGs right into a single variable, explaining 99,9% of the whole transcriptomic area. Additional TRI was linked to the properties of inhaled MWCNTs utilizing a nano-QSAR mannequin with statistics R2 = 0.83, Q2cv = 0.8, and Q2 = 0.78, which present a excessive stage of goodness-of-fit, robustness, and predictability of the obtained mannequin. By coaching a nano-QSAR mannequin on fold adjustments of hundreds of DEGs utilizing a single variable, our research considerably contributes not solely to New Strategy Methodologies (NAMs) centered on decreasing animal testing but additionally decreases the quantity of computational sources wanted for work with advanced transcriptomic knowledge. Developed throughout this work, the software program known as ChemBioML Platform (https://chembioml.com) gives researchers a strong free-to-use software for coaching regulatory acceptable Machine Studying (ML) fashions with out a robust background in programming. The ChemBioML Platform integrates the ML capabilities of Python with the superior graphical interface of Unreal Engine 5, making a bridge between scientific analysis and the sport improvement business.
