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.

GRID, FLAP, BioGPS, VolSurf+

Tortorella, S.; Carosati, E.; Sorbi, G.; Bocci, G.; Cross, S.; Cruciani, G.; Storchi, L. Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications Journal of Computational Chemistry, 2021, 42, 2068-2078. Bocci, G.; Carosati, E.; Vayer, P.; Arrault, A.; Lozano, S. Cruciani, G. ADME-Space: a new tool for medicinal chemists to explore ADME properties. Scientific Reports, 2017, 7, 1-13. Carosati, E.; van den Höfel, N.; Reif M.; Randazzo, G. M.; Stanitzki, B.; Stevens, J.; Gabbert, H. E.; Cruciani, G.; Mannhold, R.; Mahotka, C. Discovery of Novel, Potent, and Specific Cell-Death Inducers in the Jurkat Acute Lymphoblastic Leukemia Cell Line. ChemMedChem, 2015, 10: 1700-1706. Broccatelli, F.; Mannhold, R.; Moriconi, A.; Giuli, S.; Carosati, E. QSAR Modeling and Data Mining Link Torsades de Pointes Risk to the Interplay of Extent of Metabolism, Active Transport, and hERG Liability. Molecular Pharmaceutics, 2012, 9: 2290-2301. Carosati, E.; Tochowicz, A.; Marverti, G.; Guaitoli, G.; Benedetti, P.; Ferrari, S.; Stroud, R. M.; Finer-Moore, J.; Luciani, R.; Farina, D.; Cruciani, G.; Costi, M. P. Inhibitor of Ovarian Cancer Cells Growth by Virtual Screening: A New Thiazole Derivative Targeting Human Thymidylate Synthase. Journal of Medicinal Chemistry, 2012, 55: 10272-10276. Broccatelli, F.; Carosati, E.; Neri, A. Frosini, M.; Goracci, L.; Oprea, T. I.; Cruciani, G. A Novel Approach for Predicting P-glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields. Journal of Medicinal Chemistry, 2011, 54: 1740-1751. Brincat, J. P.; Carosati, E.; Sabatini, S.; Manfroni, G.; Fravolini, A.; Raygada, J. L.; Patel, D.; Kaatz, G. W.; Cruciani, G. Discovery of Novel Inhibitors of the NorA Multidrug Transporter of Staphylococcus aureus. Journal of Medicinal Chemistry, 2011, 54: 354-365. Cross, S.; Baroni, M.; Carosati, E.; Benedetti, P.; Clementi, S. FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set. Journal of Chemical Information and Modeling, 2010, 50:1442-1450. Carosati, E.; Sforna, G.; Pippi, M.; Marverti, G.; Ligabue, A.; Guerrieri, D.; Piras, S.; Guaitoli, G.; Luciani, R.; Costi, M. P.; Cruciani, G. Ligand-based virtual screening and ADME-tox guided approach to identify triazolo-quinoxalines as folate cycle inhibitors. Bioorganic & Medicinal Chemistry, 2010, 18: 7773-7785. Carosati, E.; Budriesi, R.; Ioan, P.; Ugenti, M. P.; Frosini, M.; Fusi, F.; Corda, G.; Cosimelli, B.; Spinelli, D.; Chiarini, A.; Cruciani, G. Discovery of Novel and Cardioselective Diltiazem-like Calcium Channel Blockers via Virtual Screening. Journal of Medicinal Chemistry, 2008, 51: 5552-5565. Carosati, E.; Mannhold, R.; Wahl, P.; Hansen, J. B.; Fremming, T.; Zamora, I.; Cianchetta, G.; Baroni M. Virtual Screening for Novel Openers of Pancreatic KATP Channels. Journal of Medicinal Chemistry, 2007, 50: 2117-2126. Carosati, E.; Cruciani, G.; Chiarini, A.; Budriesi, R.; Ioan, P.; Spisani, R.; Spinelli, D.; Cosimelli, B.; Fusi, F.; Frosini, M.; Matucci, R.; Gasparrini, F.; Ciogli, A.; Stephens, P. J.; Devlin, F. J. Calcium Channel Antagonists Discovered by a Multidisciplinary Approach. Journal of Medicinal Chemistry, 2006, 49: 5206-5216.

Selection of the most predictive tools. Once received the second bunch of experimental data, the set of tools elaborated in the hit optimization phase will be evaluated for their predictive power, and those which performed best will be selected. Library design. A library will be designed with the whole set of molecules of the competition, with all the predicted properties mentioned in the hit identification and hit optimization steps. A Self-Organising Map (SOM) will be used in the preliminary unsupervised analysis. Ligand-based virtual screening. A ligand-based virtual screening, using the known active as templates, will provide the LB-score for each candidate of the library. The superimposition of Molecular Interaction Fields of templates and candidates will guide the scoring functions used by the method. Pharmacophore-based virtual screening. A second screening, with a method based on the pharmacophore built on the hit optimization step and on Molecular Interactions Fields of the candidates will provide the PH-score for each candidate of the library. Structure-based virtual screening and docking. For each pocket, structure-based virtual screening and docking experiments will be carried out, and SB-score and DK-score will be obtained for each candidate of the library. Scores of different magnitude are expected for pockets of different size and shape, therefore a sort of normalization (based on a set of benchmark ligands) will be implemented to provide comparable scores. Final scoring function. An equation that combines the four previous scores (LB, PH, SB, and DK) will provide the final scores, used to rank the library candidates.

GRID, FLAP, BioGPS, VolSurf+

Tortorella, S.; Carosati, E.; Sorbi, G.; Bocci, G.; Cross, S.; Cruciani, G.; Storchi, L. Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications Journal of Computational Chemistry, 2021, 42, 2068-2078. Bocci, G.; Carosati, E.; Vayer, P.; Arrault, A.; Lozano, S. Cruciani, G. ADME-Space: a new tool for medicinal chemists to explore ADME properties. Scientific Reports, 2017, 7, 1-13. Carosati, E.; van den Höfel, N.; Reif M.; Randazzo, G. M.; Stanitzki, B.; Stevens, J.; Gabbert, H. E.; Cruciani, G.; Mannhold, R.; Mahotka, C. Discovery of Novel, Potent, and Specific Cell-Death Inducers in the Jurkat Acute Lymphoblastic Leukemia Cell Line. ChemMedChem, 2015, 10: 1700-1706. Broccatelli, F.; Mannhold, R.; Moriconi, A.; Giuli, S.; Carosati, E. QSAR Modeling and Data Mining Link Torsades de Pointes Risk to the Interplay of Extent of Metabolism, Active Transport, and hERG Liability. Molecular Pharmaceutics, 2012, 9: 2290-2301. Carosati, E.; Tochowicz, A.; Marverti, G.; Guaitoli, G.; Benedetti, P.; Ferrari, S.; Stroud, R. M.; Finer-Moore, J.; Luciani, R.; Farina, D.; Cruciani, G.; Costi, M. P. Inhibitor of Ovarian Cancer Cells Growth by Virtual Screening: A New Thiazole Derivative Targeting Human Thymidylate Synthase. Journal of Medicinal Chemistry, 2012, 55: 10272-10276. Broccatelli, F.; Carosati, E.; Neri, A. Frosini, M.; Goracci, L.; Oprea, T. I.; Cruciani, G. A Novel Approach for Predicting P-glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields. Journal of Medicinal Chemistry, 2011, 54: 1740-1751. Brincat, J. P.; Carosati, E.; Sabatini, S.; Manfroni, G.; Fravolini, A.; Raygada, J. L.; Patel, D.; Kaatz, G. W.; Cruciani, G. Discovery of Novel Inhibitors of the NorA Multidrug Transporter of Staphylococcus aureus. Journal of Medicinal Chemistry, 2011, 54: 354-365. Cross, S.; Baroni, M.; Carosati, E.; Benedetti, P.; Clementi, S. FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set. Journal of Chemical Information and Modeling, 2010, 50:1442-1450. Carosati, E.; Sforna, G.; Pippi, M.; Marverti, G.; Ligabue, A.; Guerrieri, D.; Piras, S.; Guaitoli, G.; Luciani, R.; Costi, M. P.; Cruciani, G. Ligand-based virtual screening and ADME-tox guided approach to identify triazolo-quinoxalines as folate cycle inhibitors. Bioorganic & Medicinal Chemistry, 2010, 18: 7773-7785. Carosati, E.; Budriesi, R.; Ioan, P.; Ugenti, M. P.; Frosini, M.; Fusi, F.; Corda, G.; Cosimelli, B.; Spinelli, D.; Chiarini, A.; Cruciani, G. Discovery of Novel and Cardioselective Diltiazem-like Calcium Channel Blockers via Virtual Screening. Journal of Medicinal Chemistry, 2008, 51: 5552-5565. Carosati, E.; Mannhold, R.; Wahl, P.; Hansen, J. B.; Fremming, T.; Zamora, I.; Cianchetta, G.; Baroni M. Virtual Screening for Novel Openers of Pancreatic KATP Channels. Journal of Medicinal Chemistry, 2007, 50: 2117-2126. Carosati, E.; Cruciani, G.; Chiarini, A.; Budriesi, R.; Ioan, P.; Spisani, R.; Spinelli, D.; Cosimelli, B.; Fusi, F.; Frosini, M.; Matucci, R.; Gasparrini, F.; Ciogli, A.; Stephens, P. J.; Devlin, F. J. Calcium Channel Antagonists Discovered by a Multidisciplinary Approach. Journal of Medicinal Chemistry, 2006, 49: 5206-5216.

Analysis of the experimental results. The experimental data of the selected candidates will be integrated with the unsupervised analysis model used in the final selection. A graph-representation of candidates will include experimental data and predicted properties, with layers such as pockets, chemical scaffold and molecular fragments. Structure-activity relationships. Classical medicinal chemistry SAR will be elaborated wherever possible, in case of sets of candidates sharing the same scaffold and activity data spanned over a significant range. In that case, and especially whether the active candidate has been from the in-house library, some derivatives might be synthesised in-house. For the design, all the steps described below are applicable. Graph neural network modelling. In case of activity data spread enough, the mentioned graph will be modelled by means of graph neural networks (GNN), with the aim to obtain predictive tool to run over the library of candidates to rapidly screen again the library in order to filter out candidates. Ligand-based pharmacophore definition. Provided there are some active candidates, their superimposition with a ligand-based method that uses Molecular Interaction Fields will allow the design of a pharmacophore, which will undergo docking within the corresponding pocket. The docked pharmacophore will be used for a second run of screening. Structure-based refinement. The experimental data, together with the scores used in the steps screening and docking, will guide the refinement of structure-based models used for screening and docking. Selection of candidates. All the criteria described above will guide the selection of second group of molecular candidates.

GRID, FLAP, BioGPS, VolSurf+

Tortorella, S.; Carosati, E.; Sorbi, G.; Bocci, G.; Cross, S.; Cruciani, G.; Storchi, L. Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications Journal of Computational Chemistry, 2021, 42, 2068-2078. Bocci, G.; Carosati, E.; Vayer, P.; Arrault, A.; Lozano, S. Cruciani, G. ADME-Space: a new tool for medicinal chemists to explore ADME properties. Scientific Reports, 2017, 7, 1-13. Carosati, E.; van den Höfel, N.; Reif M.; Randazzo, G. M.; Stanitzki, B.; Stevens, J.; Gabbert, H. E.; Cruciani, G.; Mannhold, R.; Mahotka, C. Discovery of Novel, Potent, and Specific Cell-Death Inducers in the Jurkat Acute Lymphoblastic Leukemia Cell Line. ChemMedChem, 2015, 10: 1700-1706. Broccatelli, F.; Mannhold, R.; Moriconi, A.; Giuli, S.; Carosati, E. QSAR Modeling and Data Mining Link Torsades de Pointes Risk to the Interplay of Extent of Metabolism, Active Transport, and hERG Liability. Molecular Pharmaceutics, 2012, 9: 2290-2301. Carosati, E.; Tochowicz, A.; Marverti, G.; Guaitoli, G.; Benedetti, P.; Ferrari, S.; Stroud, R. M.; Finer-Moore, J.; Luciani, R.; Farina, D.; Cruciani, G.; Costi, M. P. Inhibitor of Ovarian Cancer Cells Growth by Virtual Screening: A New Thiazole Derivative Targeting Human Thymidylate Synthase. Journal of Medicinal Chemistry, 2012, 55: 10272-10276. Broccatelli, F.; Carosati, E.; Neri, A. Frosini, M.; Goracci, L.; Oprea, T. I.; Cruciani, G. A Novel Approach for Predicting P-glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields. Journal of Medicinal Chemistry, 2011, 54: 1740-1751. Brincat, J. P.; Carosati, E.; Sabatini, S.; Manfroni, G.; Fravolini, A.; Raygada, J. L.; Patel, D.; Kaatz, G. W.; Cruciani, G. Discovery of Novel Inhibitors of the NorA Multidrug Transporter of Staphylococcus aureus. Journal of Medicinal Chemistry, 2011, 54: 354-365. Cross, S.; Baroni, M.; Carosati, E.; Benedetti, P.; Clementi, S. FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set. Journal of Chemical Information and Modeling, 2010, 50:1442-1450. Carosati, E.; Sforna, G.; Pippi, M.; Marverti, G.; Ligabue, A.; Guerrieri, D.; Piras, S.; Guaitoli, G.; Luciani, R.; Costi, M. P.; Cruciani, G. Ligand-based virtual screening and ADME-tox guided approach to identify triazolo-quinoxalines as folate cycle inhibitors. Bioorganic & Medicinal Chemistry, 2010, 18: 7773-7785. Carosati, E.; Budriesi, R.; Ioan, P.; Ugenti, M. P.; Frosini, M.; Fusi, F.; Corda, G.; Cosimelli, B.; Spinelli, D.; Chiarini, A.; Cruciani, G. Discovery of Novel and Cardioselective Diltiazem-like Calcium Channel Blockers via Virtual Screening. Journal of Medicinal Chemistry, 2008, 51: 5552-5565. Carosati, E.; Mannhold, R.; Wahl, P.; Hansen, J. B.; Fremming, T.; Zamora, I.; Cianchetta, G.; Baroni M. Virtual Screening for Novel Openers of Pancreatic KATP Channels. Journal of Medicinal Chemistry, 2007, 50: 2117-2126. Carosati, E.; Cruciani, G.; Chiarini, A.; Budriesi, R.; Ioan, P.; Spisani, R.; Spinelli, D.; Cosimelli, B.; Fusi, F.; Frosini, M.; Matucci, R.; Gasparrini, F.; Ciogli, A.; Stephens, P. J.; Devlin, F. J. Calcium Channel Antagonists Discovered by a Multidisciplinary Approach. Journal of Medicinal Chemistry, 2006, 49: 5206-5216.