Lead optimization data analysis techniques

Chemical process data inherently contain some degree of error, and this error may be random or systematic. Thus, the application of data reconciliation techniques allows optimal adjustment of measurement values to satisfy material and energy constraints. It also makes possible the estimation of unmeasured variables. It should be emphasized that, in today s highly competitive world market, resolving even small errors can lead to significant improvements in plant performance and economy. This book attempts to provide a comprehensive statement, analysis, and discussion of the main issues that emerge in the treatment and reconciliation of plant data. 

It is important to understand that all of these steps equally impact the success of the projea. Not understanding the system can lead to a problem definition that when solved does not help improve the system. An unreasonable hypothesis is by definition defining the wrong problem. Poorly designed experiments and ulty experimental technique leads to bad data. And improper data analysis can transform even good data into useless results. Careful attention to all steps in this process is therefore required in order to achieve optimal results. 

Therefore, a key strategy in drug discovery is to profile compoimd properties to select and optimize leads for safety and efficacy. Techniques and data analysis that contribute to tactics supporting this strategy are discussed in this chapter. 

---