Lead generation, optimization

One early step in the workflow of the medicinal chemist is to computationally search for similar compounds to known actives that are either available in internal inventory or commercially available somewhere in the world, that is, to perform similarity and substructure searches on the worldwide databases of available compounds. It is in the interest of all drug discovery programs to develop a formal process to search for such compounds and place them into the bioassays for both lead generation and analog-based lead optimization. To this end, various similarity search algorithms (both 2D and 3D) should be implemented and delivered directly to the medicinal chemist. These algorithms often prove complementary to each other in terms of the chemical diversity of the resulted compounds [8]. 

The following examples taken from the literature since 2001 are intended to illustrate the parallel optimization of some or all of the parameters discussed above, and to illustrate how some of the tools described above can aid in lead generation. It is not meant to be an exhaustive survey of the literature. The specific criteria used to define a successful lead identification campaign vary by group, as do the processes used to reach the lead stage. However, there are also many themes that are common to many of the examples below. A summary of the examples described below is contained in Table 2. 

For targets without leads, a lead generation approach is employed if work on such a target is to continue. Such approaches usually require the construction of small-molecule libraries in order to maximize the chances of finding compounds of some level of minimal potency necessary to identify a hona fide lead useful for further optimization. Lead generation library design should emphasize design quality of the individual molecules over their numbers. This topic has been discussed extensively [12]. 

Problem formulations [ 1-3 ] for designing lead-generation library under different constraints belong to a class of combinatorial resource allocation problems, which have been widely studied. They arise in many different applications such as minimum distortion problems in data compression (11), facility location problems (12), optimal quadrature rules and discretization of partial differential equations (13), locational optimization problems in control theory (9), pattern recognition (14), and neural networks... 

Lead generation is the term applied to strategies developed to identify compounds which possess a desired but non-optimized biological (IUPAC). 

The design of a robust fragment-based drug discovery (FBDD) process can lead to large increases in productivity in lead generation and lead optimization.161 It should be noted that... 

Weaver, D.C. (2004). Applying data mining techniques to library design, lead generation and lead optimization. Curr. Opin. Chem. Biol. 8, 264-270. 

After optimization, scientists test the lead compounds in more sophisticated models including pharmacokinetics, pharmacodynamics, and toxicity. The optimal molecule selected from these assessments is then declared a new dmg candidate and moves on to the next phase (development). If a program is successful, it may take a total of 3-6 years from target selection and validation through lead generation, lead optimization, and preclinical evaluation in animals to candidate selection for a potential new medicine. 

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