Optimization in medicinal chemistry often involves designing replacements for a section of a molecule which aim to retain potency while improving other properties of the compound. 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 ten 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 402 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.
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The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n°612347. The authors wish to acknowledge Lewis Vidler for constructive discussion and feedback and Jeremy Desaphy for the prepared PDB structures.
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Baumgartner, M.P., Evans, D.A. Side chain virtual screening of matched molecular pairs: a PDB-wide and ChEMBL-wide analysis. J Comput Aided Mol Des 34, 953–963 (2020). https://doi.org/10.1007/s10822-020-00313-1
- Virtual screening
- Matched pair