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Cited by 257Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic CompoundsChemical Synthesis and Analysis

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

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Hartenfeller, Markus, Zettl, Heiko, Walter, Miriam, Rupp, Matthias, Reisen, Felix, Proschak, Ewgenij, Weggen, Sascha, Stark, Holger, Schneider, Gisbert·2012·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1002380PMC PMC3280956
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Abstract

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated 'in silico' assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H₄ receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.

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Figure 1 Encoding of reactions. Example of a Paal-Knorr pyrrole reaction encoded as Reaction-MQL expression (top). Reactant substructure descriptions (left part) are separated by ‘++’. The product (right part) is separated from the reactants by ‘>> ID >>’ where ID is an arbitrary identifier of the reaction. A direct structural representation of the line notation description including atom identifiers is shown in the center. The conventional structural representation of the reaction (bottom) denotes variable parts of molecules by R-groups (Rx).
Figure 1 Encoding of reactions. Example of a Paal-Knorr pyrrole reaction encoded as Reaction-MQL expression (top). Reactant substructure descriptions (left part) are separated by ‘++’. The product (right part) is separated from the reactants by ‘>> ID >>’ where ID is an arbitrary identifier of the reaction. A direct structural representation of the line notation description including atom identifiers is shown in the center. The conventional structural representation of the reaction (bottom) denotes variable parts of molecules by R-groups (Rx).CC BY · PubMed Central
Figure 2 Two-step procedure of an extension cycle. Step 1 (left) selects the reaction by scoring generated dummy products. In the example, only two reactions can be applied (Suzuki coupling and amide coupling), and the amide dummy product scores favorable. In Step 2 (right), all reactants from the building block library exhibiting a suitable amine are added to the growing molecule via amide bond formation. The top-scoring product represents the extended intermediate product and is selected for the next design cycle.
Figure 2 Two-step procedure of an extension cycle. Step 1 (left) selects the reaction by scoring generated dummy products. In the example, only two reactions can be applied (Suzuki coupling and amide coupling), and the amide dummy product scores favorable. In Step 2 (right), all reactants from the building block library exhibiting a suitable amine are added to the growing molecule via amide bond formation. The top-scoring product represents the extended intermediate product and is selected for the next design cycle.CC BY · PubMed Central
Figure 3 Flowchart of the molecule design algorithm. (A) The stop criterion controlling the maximum number of reaction steps is excluded from the flowchart for simplification. (B) Detailed description of flowchart element B (grey circle). It comprises the key steps taken to extend intermediate product Z and yield the top-scored intermediate product Ž ( = Z grown by an additional fragment) by applying in silico reactions.
Figure 3 Flowchart of the molecule design algorithm. (A) The stop criterion controlling the maximum number of reaction steps is excluded from the flowchart for simplification. (B) Detailed description of flowchart element B (grey circle). It comprises the key steps taken to extend intermediate product Z and yield the top-scored intermediate product Ž ( = Z grown by an additional fragment) by applying in silico reactions.CC BY · PubMed Central
Figure 4 Reduced graph representation. (A) An example of a reduced graph representation. Dashed lines connect atoms or rings of the molecule (left) with their corresponding vertex of the reduced graph (right). For clarity only some lines are shown. (B) Examples of polycyclic (‘amalgamated’) substructures translated to a single vertex in the reduced graph. (C) Edges of order two are used to connect fused rings (bottom) in order to distinguish the shown cases of neighbored rings in reduced graph representation.
Figure 4 Reduced graph representation. (A) An example of a reduced graph representation. Dashed lines connect atoms or rings of the molecule (left) with their corresponding vertex of the reduced graph (right). For clarity only some lines are shown. (B) Examples of polycyclic (‘amalgamated’) substructures translated to a single vertex in the reduced graph. (C) Edges of order two are used to connect fused rings (bottom) in order to distinguish the shown cases of neighbored rings in reduced graph representation.CC BY · PubMed Central
Figure 5 Known trypsin inhibitors. Five trypsin inhibitors served as reference compounds for DOGS design runs (Camostat [45], NAPAMP [46], Efegatran [47], Patamostat [48], [49], UK-156406 [50]).
Figure 5 Known trypsin inhibitors. Five trypsin inhibitors served as reference compounds for DOGS design runs (Camostat [45], NAPAMP [46], Efegatran [47], Patamostat [48], [49], UK-156406 [50]).CC BY · PubMed Central
Figure 6 Property distributions. Comparison of property distributions between compounds designed by DOGS (left) and the reference compounds (right). ‘Rule of 5’ violations (A) and logP(o/w) values (C) were calculated using MOE. Dug-likeness scores (B) were computed by a trained neural network classifier (1 = high drug-likeness).
Figure 6 Property distributions. Comparison of property distributions between compounds designed by DOGS (left) and the reference compounds (right). ‘Rule of 5’ violations (A) and logP(o/w) values (C) were calculated using MOE. Dug-likeness scores (B) were computed by a trained neural network classifier (1 = high drug-likeness).CC BY · PubMed Central
Figure 7 Bioisosteric replacement. Side-chains addressing the S1 pocket present in the reference compounds (left) and surrogates suggested by DOGS found in top-scored 200 designs (right).
Figure 7 Bioisosteric replacement. Side-chains addressing the S1 pocket present in the reference compounds (left) and surrogates suggested by DOGS found in top-scored 200 designs (right).CC BY · PubMed Central
Figure 8 Side-chains addressing the S1 pocket of trypsin. Known inhibitors of trypsin exhibiting pyrimidin-2-amine [51] (left) and the pyridin-2-amine [52] (right) side-chains (grey circles). These moieties were also suggested by DOGS as bioisosters for side-chains of the reference ligands addressing the S1 pocket of trypsin.
Figure 8 Side-chains addressing the S1 pocket of trypsin. Known inhibitors of trypsin exhibiting pyrimidin-2-amine [51] (left) and the pyridin-2-amine [52] (right) side-chains (grey circles). These moieties were also suggested by DOGS as bioisosters for side-chains of the reference ligands addressing the S1 pocket of trypsin.CC BY · PubMed Central
Figure 9 Suggested trypsin inhibitors. Compounds 1 and 2 were proposed by the software as potential trypsin inhibitors. Reference ligands (Efegatran [47], Camostat [45]) and suggested synthesis pathways are presented for both candidate structures.
Figure 9 Suggested trypsin inhibitors. Compounds 1 and 2 were proposed by the software as potential trypsin inhibitors. Reference ligands (Efegatran [47], Camostat [45]) and suggested synthesis pathways are presented for both candidate structures.CC BY · PubMed Central
Figure 10 Automated design of γ-secretase modulators. Compounds 3 and 4 were proposed by DOGS as potential modulators of γ-secretase. Synthesis plans were suggested by the software and successfully pursued. Molecules 3 and 4 originate from distinct runs based on different reference ligands [53].
Figure 10 Automated design of γ-secretase modulators. Compounds 3 and 4 were proposed by DOGS as potential modulators of γ-secretase. Synthesis plans were suggested by the software and successfully pursued. Molecules 3 and 4 originate from distinct runs based on different reference ligands [53].CC BY · PubMed Central
Figure 11 Automated design of H4 ligands. Compounds 5 and 6 were proposed by DOGS based on an inverse agonist of hH4R [40] (A). Compound 7 is a design originating from the hH4R antagonist JNJ7777120 [54] (B).
Figure 11 Automated design of H4 ligands. Compounds 5 and 6 were proposed by DOGS based on an inverse agonist of hH4R [40] (A). Compound 7 is a design originating from the hH4R antagonist JNJ7777120 [54] (B).CC BY · PubMed Central
Figure 12 Structural features of H4R ligands. Highlighted features of known H4R ligands (compound 8 [40]: central triazole ring; compound 9 [41]: ether alkyl linker) are combined in designed compound 7. None of these features is present in the reference ligand underlying the design of 7.
Figure 12 Structural features of H4R ligands. Highlighted features of known H4R ligands (compound 8 [40]: central triazole ring; compound 9 [41]: ether alkyl linker) are combined in designed compound 7. None of these features is present in the reference ligand underlying the design of 7.CC BY · PubMed Central

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Metadata

Authors
Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G.
Affiliations
  • ETH Zurich (CH)
  • Goethe University Frankfurt (DE)
  • Heinrich Heine University Düsseldorf (DE)
Corresponding author
Schneider, Gisbert
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2012-02-16
Volume
8
Issue
2
Pages
e1002380
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1002380
PMC
PMC3280956
OpenAlex
W2052933929
Cited by
251
References
56
Citations by year
2026: 3 · 2025: 13 · 2024: 27 · 2023: 16 · 2022: 25 · 2021: 31 · 2020: 25 · 2019: 23 · 2018: 17 · 2017: 9 · 2016: 15 · 2015: 11 · 2014: 12 · 2013: 17 · 2012: 6
FWCI
15.80
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
  • ETH Zürich Research Collection (cc-by)
Funders
  • Deutsche Forschungsgemeinschaft (BM0806)
  • Ministry of Education, India
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords
de novo drug design, ligand-based drug design, synthetic building blocks, scaffold hopping, histamine H4 receptor, gamma-secretase, bioactive compounds, drug-likeness
MeSH terms
Receptors, Histamine H4, Algorithms, Automation, Computers, Humans, Ligands, Models, Chemical, Receptors, Histamine, Software, Technology, Pharmaceutical, Drug Design, Models, Statistical, Molecular Structure, Computational Biology, Receptors, G-Protein-Coupled, Amyloid Precursor Protein Secretases

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