Predictive Methods in Lead Generation

Lead generation offers an opportunity to initiate the drug discovery process with the best possible chemical starting points. In this phase, a project can survey a wide diversity of compoimds to identify novel compounds that balance potency with appropriate physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties and a low risk of toxicity. Later in the optimization process, when locked in to a small number of series, it may be much more difficult to improve one property without having a negative effect on another. 

Conversely, lead discovery presents a significant challenge due to the lack of experimental data that are available for the compounds being investigated. Due 

Lead Generation Methods, Strategies and Case Studies First Edition. Edited by Jorg Holenz. 

Intrinsic clearance in human liver microsomes (Clint) 25 pl/min/mg protein 

Avoid cytochrome P450-mediated drug-drug interactions (A) for lpM 

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Testing in lead generation and marketing is akin to the scientific method communicate a question, hypothesize the cmswer, formulate your predictions, test those predictions, and ancdyze the results. 

The computation times are much higher than in the database approach the recalculations in the modeling process must be performed on each relevant initial model found in the database. Depending on the number of operations, this leads to between approximately 500 and 5000 recalculations of new 3D models and RDF descriptors for each initial model. With respect to about 100,000 compounds in the binary database for the initial models, this can result in several million calculations per prediction if several initial models should be regarded. The method can be improved by implementation of a fast 3D structure generator into the prediction software. In this case, a reliable 3D structure is calculated after each modeling operation directly. 

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