A common task in a medicinal chemistry project is compound optimization, one approach is replacement of sections of the compound with different bioisosteres, in this way the structure-activity relationship can be understood and properties of interest, such as biochemical activity, can be improved. In this study we perform a retrospective analysis using a number of computational methods to identify active side chains amongst a pool of random decoy side chains, mimicking a similar procedure that might be undertaken in a real medicinal chemistry project. We constructed a dataset derived from public ChEMBL and PDB data by identifying all ChEMBL assays where at least one of the compounds tested has also been co-crystallized in the PDB. Additionally, we required that there be at least 10 active compounds tested in the same ChEMBL assay that are matched molecular pairs to the crystallized ligand. Using the compiled dataset consisting of sets of compounds from around 250 assays, we have tested a number of methods for scoring side chains including Spark, a bioisostere replacement tool from Cresset, molecular docking using Glide from Schrodinger, docking with Smina as well as other methods. In this work we present a comparison of the performance of these methods in discriminating active side chains from decoys as well as recommendations for circumstances when different methods should be used.

Benchmarking methods for virtual screening of match molecular pairs: A PDB-wide and ChEMBL-wide analysis

254th National Meeting (2017)

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