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CRITICAL ASSESSMENT OF COMPUTATIONAL HIT-FINDING EXPERIMENTS

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Challenge #4

Hit Identification
Method type (check all that applies)
De novo design
High-throughput docking
Description of your approach (min 200 and max 800 words)

We will use a novel method named SpaceDock that we are currently benchmarking, which consists in two steps :(i) docking 150K commercial building blocks (e.g. amines, acids, sulfonylchlorides, etc...) in the binding site of interest, (ii) assemble pairs of chemically-compatible building blocks on-the-fly according to a set of 40 common organic chemistry reactions (e.g. amide bond formation, Suzuki cross-coupling, reductive amination, etc..) and in-house developed geometric rules (distances/angles) enabling the selection of appropriate pose pairs.

Preliminary trials with 5K known ligands indicate that docking the corresponding commercial building blocks (leading in a single step to the target ligand with one of our 40 organic chemistry reactions) is indeed feasible and leads to accurate ligand poses  (rmsd < 2 Ang. in 80% of test cases, unpublished data). We have assembled a set of 125K building blocks commercially available and preselected by Enamine (Y. Moroz, Enamine, personal communication) for generating the REAL space, thereby maximizing the chance that the final product is really synthesizeable. All building blocks will be docked into the 8GCT structure according to a previously determined optimal building block docking procedure (GOLD docking, ChemPLP scoring), 20 poses will be saved for every building block, therefore enabling 400 recombination/ligand. After recombination, the pose will minimized with OpenEye SZYBKI in the binding site, using the MMFF94 forcefield and finally rescored according to 3 different scoring functions (ChemPLP, HYDEscore, protein-ligand interaction fingerprint-IFP simialrity to that of the 8GCY reference). Hits will be ranked by full IFP similarity to 8GCY first, then by polar IFP similarity to 8GCY (accounting for h-bonds and ionic bonds only), and finally by increasing HYDEscore. The best 10K hits will clustered by maximal common substructures to yield a representative/cluster for purchase and synthesis.

What makes your approach stand out from the community? (<100 words)

Our approach is unique as it enables the fast docking of an ultra-large chemical space (6 billion compounds) in less than 1 week with minimal computer resources. Docking 150K building blocks is indeed fast and scales non-linearly with the number of all possible building block 1-step recombination. We implemented a robust computational pipeline that enables to scan quickly all building block assemblies and only select that are viable for chemistry-driven assembly. Modifications (bond formation, removal of exist atoms, update of atom/bond types) are done in 3D on-the fly at a high throughput, therefore leading to fully enumerated molecules that are next minimized inside the protein.

Method Name
SpaceDock
Commercial software packages used

CCDC GOLD v.2022

OpenEye SZBYKI v.2.4

BioSolveIT HYDEscorer v.1.5

Free software packages used

IChem v.5.2.9: http://bioinfo-pharma.u-strasbg.fr/labwebsite/download.html

RDKit: Open-source cheminformatics; http://www.rdkit.org, https://github.com/rdkit/rdkit

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

interaction fingerprint similarity scoring:

  1. Marcou G. and Rognan, D. (2007) Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints. J. Chem. Inf. Model, 47, 195-207.
  2. Chalopin, M., Tesse, A., Martinez, M.C., Rognan, D., Arnal, J.-F. And Andriantsitohaina, R. (2010). Estrogen receptor alpha as a key target of red wine polyphenols action on the endothelium. PLOS One, 5, e8554
  3. De Graaf, C., Rein, C., Piwnica, D., Giordanetto, F. and Rognan, D. (2011) Structure-based identification of non-competitive ligands for two related class B G Protein-coupled receptors. ChemMedChem, 6,  2159–2169. (IF=2.83)
  4. Pallandre, J.R., Borg, C., Rognan, D., Boibessot, T., Luzet, V., yesylevskyy, S., Ramseyer, C. and Pudlo, M. (2015) Novel aminotretrazole derivatives as selective STAT3 non-peptide inhibitors. Eur. J. Med. Chem., 103, 163-174 (IF=3.90)
  5. Slynko, I., Da Silva, F., Bret, G. and Rognan, D. (2016) Docking Pose Selection by Interaction Pattern Graph Similarity: Application to the D3R Grand Challenge (2015) J. Comput.-Aided Drug Des., 30, 669-683 (IF=3.66)
  6. Da Silva Figueiredo Celestino Gomes, P., Da Silva, F., Bret, G. and Rognan, D. (2018) Ranking docking poses by graph matching of protein-ligand interactions: Lessons learned from the D3R Grand Challenge 2.  J. Comput.-Aided Drug Des., 32, 75-87 (IF=3.66)
  7. Da Silva, F., Desaphy, J. and Rognan, D. (2018) IChem: A Versatile Toolkit for Detecting, Comparing and Predicting Protein-Ligand Interactions, ChemMedChem, 13, 507-510. (IF=2.98)
  8. Rivat, C., Sar, C., Mechali, I., Dioufoulet, L., Leyris, J.P., Sonrier, C., Philipson, Y., Lucas, O., Maillé, S., Haton, H., Venteo, S., Mezghrani,A., Joly; W., Mion, J., Schmitt, M., Pattyn, A., Marmigère, F., Sokoloff, P., Carroll, P., Rognan, D.* and Valmier, J.* (2018) Inhibition of neuronal FLT3 receptor tyrosine kinase alleviates peripheral neuropathic pain in mice. Nat. Commun., 9, 1042 (IF=12.12)
  9. Fremaux, J., Venin, C., Mauran, L., Zimmer, R., Koensgen, F., Rognan, D., Bitsi, S., Jones, B., Tomas, A., Guichard, G. and Goudreau, S. R. (2019) Ureidopeptide GLP-1 analogues with prolonged activity in vivo via signal bias and altered receptor trafficking. Chem. Sci.,10, 9872-9879 (IF=9.10)
  10. Tran-Nguyen, V.-K., Bret, G. and Rognan, D. (2021) Accuracy of fast scoring functions to predict high-throughput screening data from docking poses: The simpler the better. J. Chem. Inf. Model., 67, 2788-2797 (IF=4.95)
  11. Eguida M, Rognan D. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. J. Cheminform., 13, 90 (5.51)
  12. Eguida, M., Schmitt, C., Hibert, M., Villa, P. and Rognan, D. (2022). Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J. Med. Chem., 65, 13771-13783 (IF=7.94).

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