Prioritization of Hits

The first step in prioritization is to ensure that the validated hits do not contain any undesired substructures. This can be achieved using a set of rules such as 

It is also valuable in helping to educate newer medicinal chemists and in initiating discussions. 

Overall, we have found that more compounds are rejected than accepted by medicinal chemists. If we split the compounds into three groups by popularity (using cutoffs in the normalized score of 0.33 and 0.66), we find that the compounds with high, medium, and low scores account for 13, 45, and 42% of the dataset, respectively. 

Propertff Top-ranked group Middle-ranked group Bottom-ranked group 

Molecular weight Predicted druglikeness Predicted solubility Predicted toxicity Selectivity 

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Table 15.5 shows the factors that influence the prioritization of hits by medicinal chemists. Three factors (molecular weight, CLOGP, and selectivity) appear on both sides of the table, as both more important and less important factors in the prioritization. At first, this appears contradictory but in fact it reflects the different schools of thought mentioned above, where some chemists use these factors to filter the hits, while others view problems in these areas as something that can be fixed at a later time. In our experience, most chemists do filter out compounds with extreme values of molecular weight and hydrophobicity. 

Bohm, H.J. Prediction of Binding Constants of Protein-ligands a Fast Method for the Prioritization of Hits Obtained from De-novo Design or 3D Database Search Programs. J. Comput.-Aided Mol. Des. 1998, 12, 309-323. 

Protein Ligands—A Fast Method for the Prioritization of Hits Obtained from De Novo Design or 3D Database Search Programs. 

H-J Bohm (1998) Prediction of binding constants of protein ligands A fast method for the prioritization of hits obtained from de novo design or 3D database search programs, J Comput Aided Mol Design 12(4) 309-323... 

Bohm, H.J. (1994). The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. / Comput Aided Mol Des, Vol.8, No.3, pp. 243-56 Bohm, H.J. (1998). Prediction of binding constants of protein ligands a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. / Comput Aided Mol Des, Vol.l2, No.4, pp. 309-23 Borst, P. Elferink, R.O. (2002). Mammalian ABC transporters in health and disease. Annu Rev Biodiem, Vol.71,537-92... 

In this chapter, the full BioPrint approach is described, as available from Cerep in terms of both the data set and the ability to have new compounds profiled and the results provided in the context of the BioPrint data set, including the known in vivo side effects of near neighbors in this biological space (see Section 2.5). The results for the differentiation of hit/lead compounds (see Section 2.3.2.1) sometimes use a subset of the 70-100 pharmacological assays that provide the maximum signal. Usually a decision on future work prioritization could be clearly made from the data from these subsets, saving time and money. For key reference/tool compounds, a full profile was used and is recommended to be used, as unexpected off-target activities may be found that cannot usually be predicted. 

Tab. 15.5 Factors influencing chemists prioritization of screening hits...

Scientists at Bayer implemented traffic lights for the prioritization of molecules from the HTS hits. Their score is based on solubility, lipophilicity, molecular weight, Polar Surface Area (PSA), and number of rotatable bonds. 

Binding or ligand efficiency (LE) metrics (31), in which the activity is normalized by the molecular size, have been introduced to prioritize screening hits. It has been observed by Hajduk (32) that, during the optimization of a chemical... 

Numerous computational methods have been developed to predict aqueous solubility from molecular structure (Jorgensen and Duffy, 2002 Delaney, 2005). Many of them have an accuracy of 1 log unit. Commercial solubility software is widely available and some of these products estimate the solubility-pH profile for ionizable compounds. Software is most useful for virtual screening of large libraries, prioritization of compounds prior to synthesis and scoring of HTS hits (Oprea et al., 2005). Calculated values can be used to alert teams to potential solubility issues. 

The final step is the scoring of the generated protein-ligand complex. In LUDI, the problem of prioritization of the hits is approached by a two-step procedure. First, in a selection step, a number of criteria are applied in order to remove all structures with problems. Our... 

Lobell, M., Hendrix, M., Hinzen, B., Keldenich, 1., Meier, H., Schmeck, C., Schohe-Loop, R., Wunberg, T., Hillisch, A. In silica ADMET traffic fights as a tool for the prioritization of HTS hits. ChemMedChem 2006, 1, 1229-1236. 

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