Abstract
A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.
Highlights
Novel drug discovery is generally considered to be a time-consuming and expensive process
DrugBank is useful for searching known small-compound drugs of Yes, and it contains data on a number of Food and Drug Administration (FDA)-approved small-compound drugs including dasatinib, which has been approved as an anticancer drug that targets mainly Abl and Src family kinases
10 groups participated in the contest and tackled the challenge using their own methods
Summary
Novel drug discovery is generally considered to be a time-consuming and expensive process. In LB, machine learning is used when active ligands and inactive ligands are known[14,15,16], and similarity search[17,18] or pharmacophore modeling[19,20,21] is used when only active ligands are known These techniques are theoretically expected to be useful for the discovery of promising novel drug candidates, recent studies have shown that the gold standard remains to be established. Our prior-to-the-contest analysis showed that Yes has a high sequence identity with many other protein kinases (e.g., PDBID: 1Y5725, 2SRC26, 1FMK27), of which structures were determined at high resolution This indicates that homology modeling can be effectively used to obtain the 3D structure of Yes. On the ligand point of view various open source drug discovery databases such as BindingDB28, ChEMBL29, DrugBank[30], and PubChem[31] contain medicinal chemistry data on a number of drug compounds, active and inactive compounds, and targets. The availability of bioactivity data aids realistic identification of potential hit compounds
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