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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #1

Application

HIT IDENTIFICATION

Method type (check all that applies)
De novo design
High-throughput docking
Machine learning

Description of your approach (min 200 and max 800 words)

Library. The library for the screening will be composed of a large set of purchasable molecules (from ZINC20) enriched by library of molecules that exist or are synthesisable at the Applicant’s MedChem group. Traffic-Light (TL) Pre-filtering. Different filters will address the “Traffic Light (TL) criteria”: molecular fingerprints as well as molecular descriptors (both 2D and 3D) will be used to calculate solubility in water, logD and the other TL-parameters. Additional ADME filtering. Additional filters will be set for brain permeability, including transporter-mediated efflux; predictions on models (available in-house) for hERG, BBB, efflux by PGP and BCRP will determine additional criteria for filtering out compounds. Target analysis. The 6DLO protein will be considered. Potential binding pockets will be identified by in-house available tools in order to define druggable pockets. Comparison with other proteins (with known druggable pockets) will allow to prioritise the pockets of 6DLO. In some cases (very large of very narrow pockets) additional size-based criteria may be added as filters. Screening. A reduced set of molecules will be subjected to structure-based virtual screening, using a software based on Molecular Interaction Fields of the protein pockets, already used for several (successful) virtual screening projects. Molecular candidates will be ranked through screening scores; iterations over the pockets will provide a reduced set of top-ranked molecules. Docking. A final set of a few thousands of candidates will be subjected to docking experiments on the selected pockets, by using a docking software based on Molecular Interaction Fields calculated on both the ligands and the pockets. Final selection. The top-ranked molecules will be subjected to unsupervised analysis based on circular fingerprints. The Self-Organising Map (SOM) method is able to group molecules with similar scaffold(s). All the scaffolds will be evaluated with the contribution of synthetic experts (available at the Applicant’s MedChem group) and a priority will be given to those scaffolds with higher chance to provide easier synthesisable derivatives. The presence of known toxiphores could cause exclusion of molecular candidates. Repurposing of known drugs/known actives. The same procedure of pocket-comparison (Target analysis) will include those pockets linked to known drugs or known strong binders; databases such as DrugCentral, DrugBank, ChEMBL, PDB and UNIPROT will serve for the analysis. If positive, the selected list of candidates will also include such known molecules.

Method Name
MIF-based Virtual Screening

Commercial software packages used

GRID, FLAP, BioGPS, VolSurf+

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