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  1. Article ; Online: Deep Learning in Structure-Based Drug Design.

    Anighoro, Andrew

    Methods in molecular biology (Clifton, N.J.)

    2021  Volume 2390, Page(s) 261–271

    Abstract: Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely to ... ...

    Abstract Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely to establish optimal interactions with the binding site. The current interest in machine learning algorithms based on deep neural networks encouraged the application of deep learning to SBDD related problems. This chapter covers selected works in this active area of research.
    MeSH term(s) Deep Learning ; Drug Design ; Drug Discovery ; Machine Learning ; Neural Networks, Computer
    Language English
    Publishing date 2021-11-03
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-1787-8_11
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  2. Article ; Online: Underappreciated Chemical Interactions in Protein-Ligand Complexes.

    Anighoro, Andrew

    Methods in molecular biology (Clifton, N.J.)

    2020  Volume 2114, Page(s) 75–86

    Abstract: Non-covalent interactions lie at the bases of the molecular recognition process. In medicinal chemistry, understanding how bioactive molecules interact with their target can help to explain structure-activity relationships (SAR) and improve potency of ... ...

    Abstract Non-covalent interactions lie at the bases of the molecular recognition process. In medicinal chemistry, understanding how bioactive molecules interact with their target can help to explain structure-activity relationships (SAR) and improve potency of lead compounds. In particular, computational analysis of protein-ligand complexes can help to unravel key interactions and guide structure-based drug design.The literature describing protein-ligand complexes is typically focused on few types of non-covalent interactions (e.g., hydrophobic contacts, hydrogen bonds, and salt bridges). Stacking interactions involving aromatic rings are also relatively well known to medicinal chemistry practitioners. Potency optimization efforts are often focused on targeting these interactions. However, a variety of underappreciated interactions were shown to have a relevant effect on the stabilization of protein-ligand complexes. This chapter aims at listing selected non-covalent interactions and discuss some examples on how they can impact drug design.
    MeSH term(s) Drug Design ; Drug Discovery/methods ; Ligands ; Proteins/chemistry ; Structure-Activity Relationship
    Chemical Substances Ligands ; Proteins
    Language English
    Publishing date 2020-02-03
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-0282-9_5
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  3. Article ; Online: A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity.

    Anighoro, Andrew / Bajorath, Jürgen

    Methods in molecular biology (Clifton, N.J.)

    2018  Volume 1824, Page(s) 165–175

    Abstract: In structure-based virtual screening (SBVS), a scoring function is usually applied to rank a database of docked compounds. Docking programs are often successful in reproducing experimental binding modes; however, the estimation of binding affinity still ... ...

    Abstract In structure-based virtual screening (SBVS), a scoring function is usually applied to rank a database of docked compounds. Docking programs are often successful in reproducing experimental binding modes; however, the estimation of binding affinity still is the Achilles' heel of docking. The integration of SB and ligand-based (LB) methods is considered a promising strategy to increase hit rates in VS. Herein, we describe a hybrid protocol that is based on the assessment of binding mode similarity between docked compounds and a bound reference ligand. In this context, both experimental and computationally modeled poses have been successfully used as references for three-dimensional (3D) similarity calculations. In this chapter, the methods applied in recent validation studies are described.
    MeSH term(s) Molecular Docking Simulation/methods ; Software
    Language English
    Publishing date 2018-07-23
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-8630-9_9
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  4. Article ; Online: Enhancing Chemogenomics with Predictive Pharmacology.

    James, Tim / Sardar, Adam / Anighoro, Andrew

    Journal of medicinal chemistry

    2020  Volume 63, Issue 21, Page(s) 12243–12255

    Abstract: One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and ... ...

    Abstract One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and selectivity of candidate molecules. However, this approach is profoundly limited by the sparsity of the known data, the annotation bias often found in the literature, and the cost of physical screening. Recent advancements in predictive pharmacology, such as the application of multitask and transfer learning, as well as the use of biologically motivated, structure-agnostic features to characterize molecules, should serve to mitigate these issues. Computational modeling likely offers the only cost-effective approach to substantially increasing the bioactivity annotation density both on the local and global scale and thus, we argue, will need to make a substantial contribution if the ambitious goals of probing the human proteome are to be realized in the foreseeable future.
    MeSH term(s) Computational Biology ; Drug Discovery ; High-Throughput Screening Assays ; Neural Networks, Computer
    Language English
    Publishing date 2020-07-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 218133-2
    ISSN 1520-4804 ; 0022-2623
    ISSN (online) 1520-4804
    ISSN 0022-2623
    DOI 10.1021/acs.jmedchem.0c00445
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    In stock of ZB MED Cologne/Königswinter

    Zs.B 151: Show issues Location:
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  5. Article: Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors.

    Anighoro, Andrew / Bajorath, Jürgen

    ACS omega

    2017  Volume 2, Issue 6, Page(s) 2583–2592

    Abstract: Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of "double-hypothetical" models of ligand-target complexes has intrinsic accuracy limitations that ... ...

    Abstract Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of "double-hypothetical" models of ligand-target complexes has intrinsic accuracy limitations that further complicate compound ranking and selection compared to those of X-ray structures. Given these uncertainties, we have explored "fuzzy 3D similarity" between hypothetical binding modes of known ligands in homology models and docking poses of database compounds as an alternative to conventional scoring schemes. Therefore, GPCR homology models at varying accuracy levels were generated and used for docking. Increases in recall performance were observed for fuzzy 3D similarity ranking using single or multiple ligand poses compared to that of conventional scoring functions and interaction fingerprints. Fuzzy similarity ranking was also successfully applied to docking into an external model of a GPCR for which no experimental structure is currently available. Taken together, our results indicate that the use of putative ligand poses, albeit approximate at best, increases the odds of identifying active compounds in docking screens of GPCR homology models.
    Language English
    Publishing date 2017-06-08
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN 2470-1343
    DOI 10.1021/acsomega.7b00330
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  6. Article ; Online: Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes.

    Anighoro, Andrew / Bajorath, Jürgen

    Journal of chemical information and modeling

    2016  Volume 56, Issue 3, Page(s) 580–587

    Abstract: Molecular docking is the premier approach to structure-based virtual screening. While ligand posing is often successful, compound ranking using force field-based scoring functions remains difficult. Uncertainties associated with scoring often limit the ... ...

    Abstract Molecular docking is the premier approach to structure-based virtual screening. While ligand posing is often successful, compound ranking using force field-based scoring functions remains difficult. Uncertainties associated with scoring often limit the ability to confidently identify new active compounds. In this study, we introduce an alternative approach to compound ranking. Rather than using scoring functions for final ranking, compounds are prioritized on the basis of computed 3D similarity to known crystallographic ligands. For different targets, it is shown that 3D similarity-based ranking consistently improves the enrichment of active compounds compared to ranking obtained using scoring functions, even if only a single crystallographic ligand is used as a reference. While the strategy is not applicable in cases where no cocrystal structure is available, it should be a promising alternative or complement to conventional scoring in many instances. Since ligand similarity calculations are used to rank docking poses, which are independently derived, the approach introduced herein also contributes to the integration of ligand- and structure-based computational screening methods.
    MeSH term(s) Crystallography, X-Ray ; Drug Design ; Ligands ; Molecular Dynamics Simulation ; Uncertainty
    Chemical Substances Ligands
    Language English
    Publishing date 2016-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.5b00745
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
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  7. Article ; Online: Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor.

    Anighoro, Andrew / Bajorath, Jürgen

    Journal of computer-aided molecular design

    2016  Volume 30, Issue 6, Page(s) 447–456

    Abstract: We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of ... ...

    Abstract We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor-ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.
    MeSH term(s) Adenosine A2 Receptor Antagonists/chemistry ; Algorithms ; Binding Sites ; Crystallography, X-Ray ; Drug Design ; Humans ; Ligands ; Molecular Docking Simulation ; Protein Binding ; Receptor, Adenosine A2A/chemistry
    Chemical Substances Adenosine A2 Receptor Antagonists ; Ligands ; Receptor, Adenosine A2A
    Language English
    Publishing date 2016
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 808166-9
    ISSN 1573-4951 ; 0920-654X
    ISSN (online) 1573-4951
    ISSN 0920-654X
    DOI 10.1007/s10822-016-9918-z
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    Zs.A 2625: Show issues Location:
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  8. Article: Learning protein-ligand binding affinity with atomic environment vectors.

    Meli, Rocco / Anighoro, Andrew / Bodkin, Mike J / Morris, Garrett M / Biggin, Philip C

    Journal of cheminformatics

    2021  Volume 13, Issue 1, Page(s) 59

    Abstract: Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the ... ...

    Abstract Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson's correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of [Formula: see text]-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, [Formula: see text]-AEScore has an RMSE of 1.32 pK units and a Pearson's correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.
    Language English
    Publishing date 2021-08-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2486539-4
    ISSN 1758-2946
    ISSN 1758-2946
    DOI 10.1186/s13321-021-00536-w
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  9. Article ; Online: Identification of 4-aryl-1H-pyrrole[2,3-b]pyridine derivatives for the development of new B-Raf inhibitors.

    Pinzi, Luca / Anighoro, Andrew / Bajorath, Jürgen / Rastelli, Giulio

    Chemical biology & drug design

    2018  Volume 92, Issue 1, Page(s) 1382–1386

    Abstract: During the last years, a significant interest in the identification of new classes of B-Raf inhibitors has emerged. In this study, which was conceived within an effort that culminated in the recent report of the first dual inhibitors of B-Raf and Hsp90, ... ...

    Abstract During the last years, a significant interest in the identification of new classes of B-Raf inhibitors has emerged. In this study, which was conceived within an effort that culminated in the recent report of the first dual inhibitors of B-Raf and Hsp90, we describe the identification of four compounds based on 4-aryl-1H-pyrrole[2,3-b]pyridine scaffold as interesting starting points for the development of new B-Raf inhibitors. Structure-activity relationships and predicted binding modes are discussed. Moreover, the novelty of the newly identified structures with respect to currently known B-Raf inhibitors was assessed through a ligand-based dissimilarity assessment. Finally, structural modifications with the potential ability to improve the activity toward B-Raf are put forward.
    MeSH term(s) Binding Sites ; Humans ; Hydrogen Bonding ; Inhibitory Concentration 50 ; Molecular Docking Simulation ; Protein Kinase Inhibitors/chemistry ; Protein Kinase Inhibitors/metabolism ; Protein Structure, Tertiary ; Proto-Oncogene Proteins B-raf/antagonists & inhibitors ; Proto-Oncogene Proteins B-raf/metabolism ; Pyridines/chemistry ; Pyrroles/chemistry ; Structure-Activity Relationship
    Chemical Substances Protein Kinase Inhibitors ; Pyridines ; Pyrroles ; Proto-Oncogene Proteins B-raf (EC 2.7.11.1)
    Language English
    Publishing date 2018-03-08
    Publishing country England
    Document type Letter
    ZDB-ID 2216600-2
    ISSN 1747-0285 ; 1747-0277
    ISSN (online) 1747-0285
    ISSN 1747-0277
    DOI 10.1111/cbdd.13185
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  10. Article ; Online: Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G‑Protein-Coupled Receptors

    Andrew Anighoro / Jürgen Bajorath

    ACS Omega, Vol 2, Iss 6, Pp 2583-

    2017  Volume 2592

    Keywords Chemistry ; QD1-999
    Language English
    Publishing date 2017-06-01T00:00:00Z
    Publisher American Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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