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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #2

Application

HIT IDENTIFICATION

Method type (check all that applies)
De novo design
Machine learning
Physics-based

Other (specify)
Crowd-sourced design

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

Foldit is a crowd-sourced molecular biology game. For this challenge, Foldit players will use the graphical small molecule design tools to manually add atoms, bonds and fragments to a starting ligand with the binding pocket (derived from the crystal structures with starting fragments) to optimize the designed ligand for binding into the protein pocket. A newly-implemented library search feature can then be used by the players to find make-on-demand compounds which closely match their designed ligand. Players can then use cycles of manual editing and library search to navigate chemical space and find accessible compounds which bind well to the protein and match the other specified objectives of the CACHE traffic light system. These additional objectives will be encoded into the puzzle setup, and with the predicted binding energy will contribute to the score which the Foldit players will attempt to optimize. Several rounds of design will be performed, slightly varying the system setup (e.g. differing starting structure, differing objective weighting) to get a variety of compounds.

The set of compounds thus designed by players will then be collected. Library search will be used to identify the closest on-library compounds to player designs. The library compounds will be evaluated with RDKit to rank and prioritize those compounds which best conform to the traffic light metrics. Highly ranked compounds will then be redocked into the pocket by Foldit scientists using RosettaLigand to confirm their binding mode. The list of molecules will then be prioritized by docking score, conformational feasibility, and molecular properties. Orthogonal absolute binding energy prediction (e.g. BCL-AffinityNet/BCL-DockANNScore) will also be performed to assist in compound ranking.

While we will prioritize commercially available compounds, the design methodology allows for generation off-library de novo compounds. If such compounds appear to be clearly superior to on-library compounds, we do not rule out selecting such de novo compounds.

Method Name
Drugit

Free software packages used

Foldit/Rosetta/RDKit/ZINC API/BCL/OpenBabel

Relevant publications of previous uses by your group of this software/method

Foldit Protein Design: https://doi.org/10.1038/s41586-019-1274-4

RosettaLigand ligand docking: https://doi.org/10.1371/journal.pone.0240450 https://doi.org/10.1371/journal.pone.0132508

BCL-AffinityNet/BCL-DockANNScore: https://doi.org/10.1021/acs.jcim.0c01001

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