ACD/Structure Design Suite and ACD/Structure Designer: Product Comparison

Both ACD/Structure Design Suite and ACD/Structure Designer use the same proven predictive algorithms and models to help optimize lead compounds to produce analogs with improved physicochemical characteristics. While ACD/Structure Designer offers the functionality usually requested by the synthetic chemist, ACD/Structure Design Suite provides additional capabilities that make this product a valuable tool for computational experts or chemists with advanced knowledge of structure-property relationships and physicochemical properties.

Recommended deployment:

1. Install a small number (one or two) of copies of ACD/Structure Design Suite packages for the physicochemical measurements/predictions experts, to give them the capability to fine tune the underlying predictions with the company's in-house measured values, and
2. Deploy several copies of ACD/Structure Designer to the chemists' desktops to help them in their day-to-day decision making with regard to analog selection and lead optimization.

	ACD/Structure Design Suite	ACD/Structure Designer
Uses ACD/Labs' industry-standard physicochemical predictions
Includes a database of substituents, and allows import of users' own libraries of fragments
Offers a number of parameters for optimization, and selects the substituents based on the requested selection
Uses a software Wizard to navigate through the selection process
Enumerates the list of suggested analogs and calculates the physicochemical properties of the new analogs
Offers multiple view and comparison options for evaluation of the proposed analogs, including sorting in a table, or a graph display
Allows import of chemical data from a variety of formats (up to 999 structures at a time)
Allows export of the results to many third-party applications, including Microsoft® Office applications		–
Displays detailed calculation protocols for the calculated physicochemical properties		–
Includes extensive databases of experimental physicochemical values ( pKa, logP, aqueous solubility)		–
Allows users to train the calculation protocol with their own experimental values to augment the prediction accuracy for their novel chemical classes		–
Allows users to incorporate their own models and algorithms that link chemical structures, topological descriptors, physicochemical properties, or other parameters to the desired endpoints.		–