Table 2:

Summary of the top 10 descriptors that drive POD predictions identified using feature selection analysis in the Random forest model.

DescriptorNumber	Relevant Descriptors	Description	Feature Importance Value	Descriptor Source
1	Study Type	The type of study conducted (chronic, subchronic etc.)	0.10	Study data
2	Molar Mass	Molar mass	0.06	CDK
3	Molecular Weight	Molecular weight	0.06	CDK
4	Species	Species in which the study was conducted (rat, mouse, rabbit)	0.06	Study data
5	VABC Volume Descriptor	Volume descriptor using the method implemented in the VABCVolume class.	0.05	CDK
6	Atomic Polarizabilities	Sum of the atomic polarizabilities (including implicit hydrogens)	0.05	CDK
7	Sv	2D constitutional descriptor (Sum of atomic van der Waals volumes scaled on carbon atom)	0.05	PaDEL
8	ATS0i	Autocorrelation descriptor (Broto-Moreau autocorrelation - lag 0/weighted by first ionization potential)	0.05	PaDEL
9	Si	Constitutional descriptor (Sum of first first ionization potentials scaled on carbon atom)	0.05	PaDEL
10	Sse	Constitutional descriptor (Sum of atomic Sanderson electronegativities scaled on carbon atom)	0.05	PaDEL