Using Cheminformatics in Drug Discovery

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.