Abstract:
: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.
journal_name
J Comput Aided Mol Desjournal_title
Journal of computer-aided molecular designauthors
Baumgartner MP,Evans DAdoi
10.1007/s10822-020-00313-1subject
Has Abstractpub_date
2020-09-01 00:00:00pages
953-963issue
9eissn
0920-654Xissn
1573-4951pii
10.1007/s10822-020-00313-1journal_volume
34pub_type
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journal_title:Journal of computer-aided molecular design
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journal_title:Journal of computer-aided molecular design
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journal_title:Journal of computer-aided molecular design
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