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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #1

Hit Identification
Method Name
Dynamic 3D Pharmacophores
Hybrid of the above
3D pharmacophores combined with molecular dynamics simulations, dynamic molecular interaction fields by tracing water
Other (specify)
3D Pharmacophore-based virtual screening, Water Tracing (PyRod)
Description of your approach (min 200 and max 800 words)

The project will begin with a structure - based analysis of the central binding cavity of the WDR40 domain, using molecular dynamics simulations together with in - house program PyRod [1,2] to sample interaction points in the binding pocket. Briefly, PyRod traces water molecules in protein binding cavities and generates dynamic maps describing the interaction patterns of the water molecules with respect to the protein. This identifies water molecules whose displacement with appropriate ligand moieties should result in a more favorable free energy upon ligand binding and thus increase the chance to identify high affinity ligands. The results from these analyses will be used to generate 3D pharmacophore features for 3D pharmacophore-based virtual screening of the Enamine Real database [3]. The initial list of hit compounds will firstly be filtered via molecular docking into the binding cavity and subsequent scoring of the docking poses against the initial screening pharmacophore [2,4-10]. The compounds will then further be filtered by the ACS substructure filter for removal of pan-assay interference compounds (PAINS), and by chemical diversity via Tanimoto coefficients based on Morgan fingerprints of the hits. The final filtering step will comprise visual inspection to ensure shape complementarity of hits to the binding cavity.

Commercial software packages used

LigandScout (Inte:Ligand, Austria) GOLD - (The Cambridge Crystallographic Data Centre, UK) Szybki (OpenEye, NM, USA)

Free software packages used

Desmond (deshaw research, NY, USA) OpenMM (https://openmm.org/)

Hit Optimization Methods
Method type (check all that applies)
De novo design
Machine learning
Description of your approach (min 200 and max 800 words)

3D pharmacophore models of each of the most active hits will be generated. These pharmacophores might show additional interaction points not included in the starting pharmacophore model. The binding affinity data will be related to the static 3D pharmacophores of the hit compounds to generate initial structure-activity relationship models [1]. These will be further investigated using our in-house dynamic pharmacophore (dynophore) method [2-6], based on molecular dynamics simulations of the ligands within the WD40 binding cavity. The fully-automated dynophore method analyzes the interaction pattern between ligand and protein over the course of MD simulations, generating a visual representation of the types of 3D pharmacophore feature types that occur over the course of the simulation, along with their respective statistical occurrences. Dynophores from most active compounds will be geometrically merged and chemical features weights will be trained using active compounds using different machine learning techniques. Furthermore, water-mediated interactions between ligand and protein will be analyzed to provide further insight into the interactions contributing to binding affinity. These analyses will determine the contribution of different interactions to ligand binding affinities, suggesting which chemical and structural properties to prioritize in the derivative compounds. Derivatives will be identified via two different methods. One will be to perform a shape-based analog search using the ROCS software from OpenEye [7]. The other will be the combinatorial in-silico generation of active ligand-derivatives with subsequent docking and dynophore-based scoring.

Method Name
Dynamic interaction pattern - based SAR determination followed by shape - based chemical similarity search and in-silico combinatorial derivative generation.
Commercial software packages used

LigandScout (Inte:Ligand, Austria) GOLD - (The Cambridge Crystallographic Data Centre, UK) Szybki, ROCS (OpenEye, NM, USA) Reactor (ChemAxon, Hungary)

Free software packages used

Desmond (deshaw research, NY, USA) OpenMM (https://openmm.org/) RDKit (https://github.com/rdkit/rdkit)

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