Table 1.

Comparison of Several Key Performance Measures for Traditional Descriptor-Based QSAR Approaches (1D, 2D, and 3D QSAR) vs Shape Signatures

asset	traditional descriptor-based QSAR approaches	Shape Signatures
speed	✓✓✓	✓✓✓
accuracy	✓✓	✓✓
scalability	✓	✓✓✓
	model requires reformulation as new data added	no reformulation needed as new data added
coverage	✓	✓✓✓
	descriptors must be available for chemical species	always works, i.e., organics, inorganics, organometallics, ions, etc.
sensitivity	✓	✓✓
	global model, lacks sensitivity (can also be used for local models)	local model, enhanced sensitivity
domain applicability	✓	✓✓✓
	model very sensitive to chemical (sub) structure of training set	much less sensitive to chemical (sub) structure of training set
interoperability	✓✓	✓✓✓
	integration with other QSAR models requires reformulation	fully compatible with other methods
ease of use	✓✓	✓✓✓
	preprocessing of queries requires time and know-how	no preprocessing, extremely simple to use