An easy-to-use pharmacophore modeling solution for ligand- and structure-based drug design

The Advantages of Pharmacophore Modeling

Pharmacophore modeling has demonstrated its value in drug design over the last 25 years with success in hit identification, core hopping, and lead optimization. By determining the spatial arrangement of chemical features that interact with a receptor, pharmacophore modeling helps create understanding of an unknown binding site in the absence of a protein structure. These spatial relationships can arise from ligand-based information, protein-based information, or a mixture of both. Virtual screening against these features can then identify novel compounds and chemotypes that are likely to bind to the target receptor more efficiently than docking experiments.

Easy-to-use yet powerful graphical interface:
A new, intuitive interface designed by UX experts working closely with our users provides powerful yet easy-to-use access to setup, execute, and analyze pharmacophore modeling experiments. A user can move seamlessly between hypotheses creation from protein-ligand complexes or only ligands, validation, modification, and screening with expert-level control as desired.

Universally applicable - Create Hypotheses from one or more ligands, protein-ligand complexes, and apo proteins:
Phase is well suited to drug discovery projects with and without receptor structures. Create hypotheses from protein-ligand complexes and apo proteins with Schrödinger's unique e-Pharmacophores technology or via observed interactions. For ligand based projects create hypotheses through common pharmacophore perception, from aligned known actives/inactives, or from particular ligands. Selectively merge hypotheses features from protein-ligand complexes and ligand-only to create hybrid models. Add your own features to hypothesis for complete control.

A unique common pharmacophore perception algorithm designed for use in both lead optimization and virtual screening:
Phase employs a newly developed common pharmacophore perception algorithm that flips the old paradigm by identifying ligand alignments first and then perceiving hypothesis. Using pharmacophore-based shape alignments, It quickly creates high-quality hypothesis from a handful to hundreds of known active ligands. A new scoring function, PhaseHypoScore, rank-orders hypotheses by their likely performance in virtual screening as well as the quality of ligand alignment. Easily recognize multiple binding modes in hypotheses from common pharmacophore perception when training against diverse known actives.

Many opportunities to introduce experimental data or user preferences:
While Phase can be used “out of the box" to quickly design and execute pharmacophore modeling experiments, it also allows users the option to exercise precise control over job settings at all steps, including pharmacophore creation, and screening. This enables users to fine-tune hypotheses creation and screening to bias results toward experimental observables.

Flexible creation and application of compound databases:
Phase uses Schrödinger's ConfGen and Epik for rapid and thorough sampling of conformational, ionization, and tautomeric space, with optional minimization using the best-in-class OPLS3 force field. Generation and updating of Phase databases can be efficiently distributed over all available computational resources. Phase databases can be used with all Schrödinger virtual screening methods including Glide, Phase, and Shape Screening. Multiple databases can be used in a single Phase screening calculation.

Fully prepared databases of purchasable compounds from Enamine, MilliporeSigma, and MolPort:
Schrödinger has partnered with Enamine, MilliporeSigma, and MolPort to provide a Phase database of fragments, lead-like, near drug-like, and drug-like compounds available from Enamine's "Stock Screening Compounds Collection", MilliporeSigma's "Aldrich Market Select", and MolPort's "Screening Compound Database" respectively. The databases can be updated quarterly to ensure compound availability and enable out-of-the-box virtual screening. Top-ranked compounds from a virtual screen can be easily purchased by ID directly from the compound vendors.

Citations and Acknowledgements

Schrödinger Release 2021-4: Phase, Schrödinger, LLC, New York, NY, 2021.

ö Dixon, S.L.; Smondyrev, A.M.; Knoll, E.H.; Rao, S.N.; Shaw, D.E.; Friesner, R.A., "PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and 3D Database Screening. 1. Methodology and Preliminary Results," J. Comput. Aided Mol. Des., 2006, 20, 647-671

ö Dixon, S.L.; Smondyrev, A.M.; Rao, S.N., "PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching," Chem. Biol. Drug Des., 2006, 67, 370-372

ö "A Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding"

Miller, E.; Murphy, R.; Sindhikara, D.; Borrelli, K.; Grisewood, M.; Ranalli, F.; Dixon, S.; Jerome, S.; Boyles, N.; Day, T.; Ghanakota, P.; Mondal, S.; Rafi, S.B.; Troast, D.M.; Abel, R.; Friesner, R.A., ChemRxiv, 2020, Preprint, 1

"Computational Tool for Fast In silico Evaluation of hERG K+ Channel Affinity"

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"Identification of Novel Fluorescent Probes Preventing PrPSc Replication in Prion Diseases"

Zaccagnini, L.; Brogi, S.; Brindisi, M.; Gemma, S.; Chemi, G.; Legname, G.; Campiani, G.; Butini, S., European Journal of Medicinal Chemistry, 2017, 127 (15), 859–873

"Discovery and Structure Activity Relationships of a Highly Selective Butyrylcholinesterase Inhibitor by Structure-Based Virtual Screening"

Dighe, S.N.; Deora,G.S.; Mora, E.; Nachon, F.; Chan, S.; Parat, M.; Brazzolotto, X.; Ross, B.P., J. Med. Chem., 2016, 59, 7683−7689

"Exploring Clotrimazole-based Pharmacophore: 3D-QSAR Studies and Synthesis of Novel Antiplasmodial Agents"

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"A Comprehensive Ligand Based Mapping of the σ2 Receptor Binding Pocket"

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"Discovery of Thienoquinolone Derivatives as Selective and ATP Non-Competitive CDK5/p25 Inhibitors by Structure-Based Virtual Screening"

Chatterjee, A.; Cutler, S.J.; Doerksen, R.J.; Khan, I.A.; Williamson, J.S., Bioorg. Med. Chem., 2014, 22, 6409-6421

"Optimization, Pharmacophore Modeling and 3D-QSAR Studies of Sipholanes as Breast Cancer Migration and Proliferation Inhibitors"

Foudah, A.I.; Sallam, A.A.; Akl, M.R.; El Sayed, K.A., Eur. J. Med. Chem., 2014, 73, 310-324

"Multiple e-pharmacophore modeling combined with high-throughput virtual screening and docking to identify potential inhibitors of β-Secretase(BACE1)"

Palakurti, R.; Sriram, D.; Yogeeswari, P.; Vadrevu, R., Mol. Inf., 2013, 32, 385-398

"Three-dimensional quantitative structure-selectivity relationships analysis guided rational design of a highly selective ligan for the cannabiniod receptor 2"

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ö "Rapid Shape-Based Ligand Alignment and Virtual Screening Method Based on Atom/Feature-Pair Similarities and Volume Overlap Scoring"

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ö "IDSite: An Accurate Approach to Predict P450-Mediated Drug Metabolism"

Li, J.; Schneebeli, S. T.; Bylund, Farid, R.; Friesner, R.A., J. Chem. Theory Comput., 2011, 7, 3829–3845

"Pharmacophore Mapping and Electronic Feature Analysis for a Series of Nitroaromatic Compounds with Antitubercular Activity"

Tawari, N. R.; Degani, M. S., J. Comput. Chem., 2010, 31, 739–751

"Insight into Inhibitory Activity of Mycobacterial Dihydrofolate Reductase Inhibitors by In-silico Molecular Modeling Approaches"

Bag, S.; Tawari, N. R.; Degani, M. S., QSAR Comb. Sci., 2009, 28, 296-311

"Synthesis, Activity, and Pharmacophore Development for Isatin-β-thiosemicarbazones with Selective Activity toward Multidrug-Resistant Cells "

Hall, M. D.; Salam, N. K.; Hellawell, J. L.; Fales, H. M.; Kensler, C. B.; Ludwig, J. A.; Szakács, G.; Hibbs, D. E.; Gottesman, M. M., J. Med. Chem. , 2009, 52, 3191–3204

"Overcoming Undesirable hERG Potency of Chemokine Receptor Antagonists Using Baseline Lipophilicity Relationships"

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"Technique for Generating Three-Dimensional Alignments of Multiple Ligands from One-Dimensional Alignments"

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"Pharmacophore mapping of a series of pyrrolopyrimidines, indolopyrimidines and their congeners as multidrug resistance-associated protein (MRP1) modulators"

Tawari, N. R.; Bag, S.; Degani, M. S., J. Mol. Model., 2008, 14, 911–921

"Identification of Plasmodium falciparum Spermidine Synthase Active Site Binders through Structure-Based Virtual Screening"

Jacobsson, M.; Garedal, M.; Schultz, J.; Karle?n, A., J. Med. Chem., 2008, 51, 2777–2786

"Novel γ-Aminobutyric Acid ρ1 Receptor Antagonists; Synthesis, Pharmacological Activity and Structure−Activity Relationships"

Kumar, R. J.; Chebib, M.; Hibbs, D. E.; Kim, H.; Johnston, G. A. R.; Salam, N. K.; Hanrahan, J. R., J. Med. Chem., 2008, 51, 3825-3840

"FTree query construction for virtual screening: a statistical analysis"

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"3-D QSAR and molecular docking studies on aryl benzofuran-2-yl ketoxime derivatives as candida albicans N-myristoyl transferase inhibitors"

Telvekar, V.; Kundaikar, H.; Patel, K.; Chaudhari, H., QSAR and Combinatorial Science, 2008, 27, 1193-1203

"QSAR models for prediction of glycogen synthase kinase-3β inhibitory activity of indirubin derivatives"

Lather, V.; Kristam, R.; Saini, J.; Karthikeyan, N.; Balaji, V., QSAR and Combinatorial Science, 2008, 27, 718-728

"Pharmacophore Refinement and 3D-QSAR Studies of Histamine H3 Antagonists"

Narkhede, S. S.; Degani, M. S., QSAR Comb. Sci., 2007, 26, 744–753

"3D QSAR Methods: Phase and Catalyst Compared"

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ö "PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching"

Dixon, S.L.; Smondyrev, A.M.; Rao, S.N., Chem. Biol. Drug Des., 2006, 67, 370-372

ö "PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and 3D Database Screening. 1. Methodology and Preliminary Results"

Dixon, S.L.; Smondyrev, A.M.; Knoll, E.H.; Rao, S.N.; Shaw, D.E.; Friesner, R.A., J. Comput. Aided Mol. Des., 2006, 20, 647-671
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