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Hua Y, Huang D, Liang L, Qian X, Dai X, Xu Y, Qiu H, Lu T, Liu H, Chen Y, Zhang Y. FSDscore: An Effective Target-focused Scoring Criterion for Virtual Screening. Mol Inform 2023; 42:e2200039. [PMID: 36372777 DOI: 10.1002/minf.202200039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 11/12/2022] [Indexed: 11/15/2022]
Abstract
Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target-focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R-groups of known ligands. This criterion is based on both ligand- and structure-based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand- and structure-based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine-protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability.
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Affiliation(s)
- Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Dingfang Huang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xu Qian
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xiaowen Dai
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yuan Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Haodi Qiu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
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2
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Atz K, Guba W, Grether U, Schneider G. Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods Mol Biol 2023; 2576:477-493. [PMID: 36152211 DOI: 10.1007/978-1-0716-2728-0_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
- ETH Singapore SEC Ltd, Singapore, Singapore
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3
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Warr WA, Nicklaus MC, Nicolaou CA, Rarey M. Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 2022; 62:2021-2034. [PMID: 35421301 DOI: 10.1021/acs.jcim.2c00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
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Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Crewe, Cheshire CW4 7HZ, United Kingdom
| | - Marc C Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Christos A Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Matthias Rarey
- Universität Hamburg, ZBH Center for Bioinformatics, 20146 Hamburg, Germany
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4
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Nigam A, Pollice R, Aspuru-Guzik A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. DIGITAL DISCOVERY 2022; 1:390-404. [PMID: 36091415 PMCID: PMC9358752 DOI: 10.1039/d2dd00003b] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/03/2022] [Indexed: 12/30/2022]
Abstract
We present JANUS, an evolutionary algorithm for inverse molecular design. It propagates an explorative and an exploitative population exchanging members via parallel tempering and uses active learning via deep neural networks to enhance sampling.
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Affiliation(s)
- AkshatKumar Nigam
- Department of Computer Science, Stanford University, USA
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G, Canada
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5
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Rica E, Álvarez S, Serratosa F. Ligand-Based Virtual Screening Based on the Graph Edit Distance. Int J Mol Sci 2021; 22:12751. [PMID: 34884555 PMCID: PMC8658044 DOI: 10.3390/ijms222312751] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 11/25/2022] Open
Abstract
Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets-CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS-have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.
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Affiliation(s)
- Elena Rica
- Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain; (S.Á.); (F.S.)
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6
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Deep Learning Applied to Ligand-Based De Novo Drug Design. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:273-299. [PMID: 34731474 DOI: 10.1007/978-1-0716-1787-8_12] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.
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7
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Chopra B, Dhingra AK. Natural products: A lead for drug discovery and development. Phytother Res 2021; 35:4660-4702. [PMID: 33847440 DOI: 10.1002/ptr.7099] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 03/01/2021] [Accepted: 03/09/2021] [Indexed: 12/29/2022]
Abstract
Natural products are used since ancient times in folklore for the treatment of various ailments. Plant-derived products have been recognized for many years as a source of therapeutic agents and structural diversity. A literature survey has been carried out to determine the utility of natural molecules and their modified analogs or derivatives as pharmacological active entities. This review presents a study on the importance of natural products in terms of drug discovery and development. It describes how the natural components can be utilized after small modifications in new perspectives. Various new modifications in structure offer a unique opportunity to establish a new molecular entity with better pharmacological potential. It was concluded that in this current era, new attempts are taken to utilize the compounds derived from natural sources as novel drug candidates, with a focus to find and discover new effective molecules that were referred to as "new entities of natural product drug discovery."
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Affiliation(s)
- Bhawna Chopra
- Department of Pharmaceutical Chemistry, Guru Gobind Singh College of Pharmacy, Yamuna Nagar, India
| | - Ashwani Kumar Dhingra
- Department of Pharmaceutical Chemistry, Guru Gobind Singh College of Pharmacy, Yamuna Nagar, India
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8
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Application of the Movable Type Free Energy Method to the Caspase-Inhibitor BindingAffinity Study. Int J Mol Sci 2019; 20:ijms20194850. [PMID: 31569580 PMCID: PMC6801467 DOI: 10.3390/ijms20194850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 09/24/2019] [Accepted: 09/25/2019] [Indexed: 11/16/2022] Open
Abstract
Many studies have provided evidence suggesting that caspases not only contribute to the neurodegeneration associated with Alzheimer’s disease (AD) but also play essential roles in promoting the underlying pathology of this disease. Studies regarding the caspase inhibition draw researchers’ attention through time due to its therapeutic value in the treatment of AD. In this work, we apply the “Movable Type” (MT) free energy method, a Monte Carlo sampling method extrapolating the binding free energy by simulating the partition functions for both free-state and bound-state protein and ligand configurations, to the caspase-inhibitor binding affinity study. Two test benchmarks are introduced to examine the robustness and sensitivity of the MT method concerning the caspase inhibition complexing. The first benchmark employs a large-scale test set including more than a hundred active inhibitors binding to caspase-3. The second benchmark includes several smaller test sets studying the relative binding free energy differences for minor structural changes at the caspase-inhibitor interaction interfaces. Calculation results show that the RMS errors for all test sets are below 1.5 kcal/mol compared to the experimental binding affinity values, demonstrating good performance in simulating the caspase-inhibitor complexing. For better understanding the protein-ligand interaction mechanism, we then take a closer look at the global minimum binding modes and free-state ligand conformations to study two pairs of caspase-inhibitor complexes with (1) different caspase targets binding to the same inhibitor, and (2) different polypeptide inhibitors targeting the same caspase target. By comparing the contact maps at the binding site of different complexes, we revealed how small structural changes affect the caspase-inhibitor interaction energies. Overall, this work provides a new free energy approach for studying the caspase inhibition, with structural insight revealed for both free-state and bound-state molecular configurations.
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9
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Garcia-Hernandez C, Fernández A, Serratosa F. Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure. J Chem Inf Model 2019; 59:1410-1421. [PMID: 30920214 PMCID: PMC6668628 DOI: 10.1021/acs.jcim.8b00820] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Extended
reduced graphs provide summary representations of chemical
structures using pharmacophore-type node descriptions to encode the
relevant molecular properties. Commonly used similarity measures using
reduced graphs convert these graphs into 2D vectors like fingerprints,
before chemical comparisons are made. This study investigates the
effectiveness of a graph-only driven molecular comparison by using
extended reduced graphs along with graph edit distance methods for
molecular similarity calculation as a tool for ligand-based virtual
screening applications, which estimate the bioactivity of a chemical
on the basis of the bioactivity of similar compounds. The results
proved to be very stable and the graph editing distance method performed
better than other methods previously used on reduced graphs. This
is exemplified with six publicly available data sets: DUD-E, MUV,
GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical
tools available on the ligand-based virtual screening benchmarking
platform and the RDKit were also used. In the experiments, our method
performed better than other molecular similarity methods which use
array representations in most cases. Overall, it is shown that extended
reduced graphs along with graph edit distance is a combination of
methods that has numerous applications and can identify bioactivity
similarities in a structurally diverse group of molecules.
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Affiliation(s)
- Carlos Garcia-Hernandez
- Departament d'Enginyeria Química , Universitat Rovira i Virgili , Tarragona , Catalunya 43007 , Spain
| | - Alberto Fernández
- Departament d'Enginyeria Química , Universitat Rovira i Virgili , Tarragona , Catalunya 43007 , Spain
| | - Francesc Serratosa
- Departament d'Enginyeria Informàtica i Matemàtiques , Universitat Rovira i Virgili , Tarragona , Catalunya 43007 , Spain
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10
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Afolabi LT, Saeed F, Hashim H, Petinrin OO. Ensemble learning method for the prediction of new bioactive molecules. PLoS One 2018; 13:e0189538. [PMID: 29329334 PMCID: PMC5766097 DOI: 10.1371/journal.pone.0189538] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/27/2017] [Indexed: 12/31/2022] Open
Abstract
Pharmacologically active molecules can provide remedies for a range of different illnesses and infections. Therefore, the search for such bioactive molecules has been an enduring mission. As such, there is a need to employ a more suitable, reliable, and robust classification method for enhancing the prediction of the existence of new bioactive molecules. In this paper, we adopt a recently developed combination of different boosting methods (Adaboost) for the prediction of new bioactive molecules. We conducted the research experiments utilizing the widely used MDL Drug Data Report (MDDR) database. The proposed boosting method generated better results than other machine learning methods. This finding suggests that the method is suitable for inclusion among the in silico tools for use in cheminformatics, computational chemistry and molecular biology.
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Affiliation(s)
| | - Faisal Saeed
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
- Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Haslinda Hashim
- Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
- Kolej Yayasan Pelajaran Johor, KM16, Jalan Kulai-Kota Tinggi, Kota Tinggi, Johor, Malaysia
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11
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Allen WJ, Fochtman BC, Balius TE, Rizzo RC. Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets. J Comput Chem 2017; 38:2641-2663. [PMID: 28940386 DOI: 10.1002/jcc.25052] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/03/2017] [Indexed: 12/12/2022]
Abstract
De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- William J Allen
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794
| | - Brian C Fochtman
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, 11794
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, 94158
| | - Robert C Rizzo
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794.,Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, 11794.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, 11794
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12
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13
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Ramírez D. Computational Methods Applied to Rational Drug Design. THE OPEN MEDICINAL CHEMISTRY JOURNAL 2016; 10:7-20. [PMID: 27708723 PMCID: PMC5039900 DOI: 10.2174/1874104501610010007] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Revised: 01/27/2016] [Accepted: 01/28/2016] [Indexed: 11/22/2022]
Abstract
Due
to the synergic relationship between medical chemistry, bioinformatics and
molecular simulation, the development of new accurate computational tools for
small molecules drug design has been rising over the last years. The main result
is the increased number of publications where computational techniques such as
molecular docking, de novo design as well as virtual screening have been
used to estimate the binding mode, site and energy of novel small molecules. In
this work I review some tools, which enable the study of biological systems at
the atomistic level, providing relevant information and thereby, enhancing the
process of rational drug design.
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Affiliation(s)
- David Ramírez
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla, Talca, Chile
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14
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Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Mol Inform 2015; 35:3-14. [PMID: 27491648 DOI: 10.1002/minf.201501008] [Citation(s) in RCA: 309] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
Abstract
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
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Affiliation(s)
- Erik Gawehn
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
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15
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Gisbert Schneider. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201409126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Gisbert Schneider. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/anie.201409126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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17
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18
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Facile and efficient preparation of hybrid phenylthiazolyl-1,3,5-triazines and their antidepressant-like effect in mice. Tetrahedron Lett 2014. [DOI: 10.1016/j.tetlet.2014.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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19
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Fisher M, Basak R, Kalverda AP, Fishwick CWG, Bruce Turnbull W, Nelson A. Discovery of novel FabF ligands inspired by platensimycin by integrating structure-based design with diversity-oriented synthetic accessibility. Org Biomol Chem 2014; 12:486-94. [DOI: 10.1039/c3ob41975d] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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20
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Ehrlich HC, Henzler AM, Rarey M. Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. J Chem Inf Model 2013; 53:1676-88. [PMID: 23751070 DOI: 10.1021/ci400107k] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Retrieving molecules with specific structural features is a fundamental requirement of today's molecular database technologies. Estimates claim the chemical space relevant for drug discovery to be around 10⁶⁰ molecules. This figure is many orders of magnitude larger than the amount of molecules conventional databases retain today and will store in the future. An elegant description of such a large chemical space is provided by the concept of fragment spaces. A fragment space comprises fragments that are molecules with open valences and describes rules how to connect these fragments to products. Due to the combinatorial nature of fragment spaces, a complete enumeration of its products is intractable. We present an algorithm to search fragment spaces for generic chemical patterns as present in the SMARTS chemical pattern language. Our method allows specification of the chemical surrounding of an atom in a query and, therefore, enables a chemically intuitive search. During the search, the costly enumeration of products is avoided. The result is a fragment space that exactly describes all possible molecules that contain the user-defined pattern. We evaluated the algorithm in three different drug development use-cases and performed a large scale statistical analysis with 738 SMARTS patterns on three public available fragment spaces. Our results show the ability of the algorithm to explore the chemical space around known active molecules, to analyze fragment spaces for the presence of likely toxic molecules, and to identify complex macromolecular structures under additional structural constraints. By searching the fragment space in its nonenumerated form, spaces covering up to 10¹⁹ molecules can be examined in times ranging between 47 s and 19 min depending on the complexity of the query pattern.
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21
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Westermaier Y, Veurink M, Riis-Johannessen T, Guinchard S, Gurny R, Scapozza L. Identification of aggregation breakers for bevacizumab (Avastin®) self-association through similarity searching and interaction studies. Eur J Pharm Biopharm 2013; 85:773-80. [PMID: 23665445 DOI: 10.1016/j.ejpb.2013.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Revised: 04/16/2013] [Accepted: 04/19/2013] [Indexed: 10/26/2022]
Abstract
Aggregation is a common challenge in the optimization of therapeutic antibody formulations. Since initial self-association of two monomers is typically a reversible process, the aim of this study is to identify different excipients that are able to shift this equilibrium to the monomeric state. The hypothesis is that a specific interaction between excipient and antibody may hinder two monomers from approaching each other, based on previous work in which dexamethasone phosphate showed the ability to partially reverse formed aggregates of the monoclonal IgG1 antibody bevacizumab back into monomers. The current study focuses on the selection of therapeutically inactive compounds with similar properties. Adenosine monophosphate, adenosine triphosphate, sucrose-6-phosphate and guanosine monophosphate were selected in silico through similarity searching and docking. All four compounds were predicted to bind to a protein-protein interaction hotspot on the Fc region of bevacizumab and thereby breaking dimer formation. The predictions were supported in vitro: An interaction between AMP and bevacizumab with a dissociation constant of 9.59±0.15 mM was observed by microscale thermophoresis. The stability of the antibody at elevated temperature (40 °C) in a 51 mM phosphate buffer pH 7 was investigated in presence and absence of the excipients. Quantification of the different aggregation species by asymmetrical flow field-flow fractionation and size exclusion chromatography demonstrates that all four excipients are able to partially overcome the initial self-association of bevacizumab monomers.
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Affiliation(s)
- Y Westermaier
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva 4, Switzerland
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Ehrlich HC, Volkamer A, Rarey M. Searching for Substructures in Fragment Spaces. J Chem Inf Model 2012. [DOI: 10.1021/ci300283a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Andrea Volkamer
- University of Hamburg, Bundestraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- University of Hamburg, Bundestraße 43, 20146 Hamburg, Germany
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Munichandrababu T, Bhaskar BV, Ravi S, Bhuvaneswar C, Rajendra W. Structure based virtual screening of non-steroidal anti-inflammatory drugs (NSAIDs) against RNA-binding motif 6 (RBM6) involved in human lung cancer. Med Chem Res 2012. [DOI: 10.1007/s00044-012-0276-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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24
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Reymond JL, Ruddigkeit L, Blum L, van Deursen R. The enumeration of chemical space. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1104] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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25
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DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 2012; 8:e1002380. [PMID: 22359493 PMCID: PMC3280956 DOI: 10.1371/journal.pcbi.1002380] [Citation(s) in RCA: 162] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Accepted: 12/21/2011] [Indexed: 11/19/2022] Open
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. The computer program DOGS aims at the automated generation of new bioactive compounds. Only a single known reference compound is required to have the computer come up with suggestions for potentially isofunctional molecules. A specific feature of the algorithm is its capability to propose a synthesis plan for each designed compound, based on a large set of readily available molecular building blocks and established reaction protocols. The de novo design software provides rapid access to tool compounds and starting points for the development of a lead candidate structure. The manuscript gives a detailed description of the algorithm. Theoretical analysis and prospective case studies demonstrate its ability to propose bioactive, plausible and chemically accessible compounds.
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Nargotra A, Sharma S, Alam MI, Ahmed Z, Bhagat A, Taneja SC, Qazi GN, Koul S. In silico identification of viper phospholipaseA2 inhibitors: validation by in vitro, in vivo studies. J Mol Model 2011; 17:3063-73. [PMID: 21360175 DOI: 10.1007/s00894-011-0994-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Accepted: 01/25/2011] [Indexed: 02/08/2023]
Abstract
Snake venom, particularly of vipers from the Indian subcontinent, contains Phospholipase A2 (PLA2) as one its constituents which is widely implicated in hemorrhagic, cardiac arrest and death. Development of inhibitors of the protein can facilitate the weakening or annihilation of the venom toxicity and save many human lives. In the present communication, our studies relate to the design and development of structure-based ligands as inhibitors of PLA2 of Viper venom. The study involves the computational approach towards evaluating a library of molecules comprising of natural products, and synthetic molecules through docking studies on the venom protein PDB ID: 1OXL (a dimer, available in the literature). In silico experiments have resulted in the identification of several of them as PLA2 inhibitors. The inhibitory effect of PLA2 by these compounds is attributed to a great extent to their interaction with the residues Phe 46 and Val47 of chain B of the target protein and hence these two residues are identified as the key contributor for the said activity. In order to validate the in silico findings, a selected panel of compounds have been tested by in vitro and in vivo experiments against the venom, which has led to the observance of significant corroboration between the wet lab and in silico findings, validating thereby the in silico approach used in the present study.
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Affiliation(s)
- Amit Nargotra
- Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India
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27
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Schneider G. Designing the molecular future. J Comput Aided Mol Des 2011; 26:115-20. [DOI: 10.1007/s10822-011-9485-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2011] [Accepted: 11/03/2011] [Indexed: 10/15/2022]
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28
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Singh S, Gupta SK, Nischal A, Khattri S, Nath R, Pant KK, Seth PK. Identification and Characterization of Novel Small-Molecule Inhibitors against Hepatitis Delta Virus Replication by Using Docking Strategies. HEPATITIS MONTHLY 2011; 11:803-809. [DOI: 10.5812/kowsar.1735143x.1387] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
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Schneider P, Stutz K, Kasper L, Haller S, Reutlinger M, Reisen F, Geppert T, Schneider G. Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library. Pharmaceuticals (Basel) 2011. [PMCID: PMC4058656 DOI: 10.3390/ph4091236] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.
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Affiliation(s)
| | | | | | | | | | | | | | - Gisbert Schneider
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +41-44-633-7438; Fax: +41-44-633-1379
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Gisbert Schneider. ChemMedChem 2011. [DOI: 10.1002/cmdc.201100217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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31
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Koeppen H, Kriegl J, Lessel U, Tautermann CS, Wellenzohn B. Ligand-Based Virtual Screening. METHODS AND PRINCIPLES IN MEDICINAL CHEMISTRY 2011. [DOI: 10.1002/9783527633326.ch3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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32
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Hartenfeller M, Schneider G. Enabling future drug discovery by
de novo
design. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.49] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Markus Hartenfeller
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
| | - Gisbert Schneider
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
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Schneider G, Geppert T, Hartenfeller M, Reisen F, Klenner A, Reutlinger M, Hähnke V, Hiss JA, Zettl H, Keppner S, Spänkuch B, Schneider P. Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med Chem 2011; 3:415-24. [PMID: 21452978 DOI: 10.4155/fmc.11.8] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2023] Open
Abstract
BACKGROUND De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis. METHODS We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro. RESULTS The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested. CONCLUSIONS Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.
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Affiliation(s)
- Gisbert Schneider
- Swiss Federal Institute of Technology, Department of Chemistry & Applied Biosciences, 8093 Zürich, Switzerland.
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Abstract
Low-throughput screening for bioactive substances often represents the only way to discover new ligands of a drug target. This limits the number of compounds that can be tested for bioactivity. In such a situation, the design of small, focused compound libraries provides an alternative to the concept of large, maximally diverse screening collections. We present the technique of "adaptive" compound library design, which implements a simulated evolutionary process. Compound assembly and determination of bioactivity can be performed using computer-based methods (virtual screening), or in the laboratory. We show that there exists an optimal combination of the size of a screening library and the number of iterative screening rounds with the aim to keep experimental efforts at a minimum.
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35
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Pérez‐Nueno VI, Ritchie DW. Applying in silico tools to the discovery of novel CXCR4 inhibitors. Drug Dev Res 2010. [DOI: 10.1002/ddr.20406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Violeta I. Pérez‐Nueno
- INRIA Nancy – Grand Est, LORIA (Laboratoire Lorrain de Recherche en Informatique et ses Applications), Vandoeuvre‐les‐Nancy, France
| | - David W. Ritchie
- INRIA Nancy – Grand Est, LORIA (Laboratoire Lorrain de Recherche en Informatique et ses Applications), Vandoeuvre‐les‐Nancy, France
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Senger S. Using Tversky similarity searches for core hopping: finding the needles in the haystack. J Chem Inf Model 2009; 49:1514-24. [PMID: 19453147 DOI: 10.1021/ci900092y] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The combination of Daylight fingerprints and the Tversky coefficient is a powerful method for performing core hopping, that is, scaffold (or lead) hopping where the main structural difference between the query and bioactive target molecule is located in the central core of the molecular structure. However, a major disadvantage of this approach is the fact that a large number of false positives (in the context of core hopping) are retrieved. The tool we have developed and which is described here can be used to postprocess the hits from Daylight Tversky similarity searches by fragmenting the molecules and subsequently annotating them in a way that assists the users in removing false positives and enables them to better focus on molecules of interest. To validate our approach, we have selected four biological targets for which scaffold hopping examples have been reported. We present results from searches in databases containing published activity data and the subsequent analysis of the hits aimed at establishing the potential of our approach.
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Affiliation(s)
- Stefan Senger
- GlaxoSmithKline, Medicines Research Centre, Stevenage SG1 2NY, United Kingdom.
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Proschak E, Sander K, Zettl H, Tanrikulu Y, Rau O, Schneider P, Schubert‐Zsilavecz M, Stark H, Schneider G. From Molecular Shape to Potent Bioactive Agents II: Fragment‐Based de novo Design. ChemMedChem 2009; 4:45-8. [DOI: 10.1002/cmdc.200800314] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Ewgenij Proschak
- Goethe‐University, Institute of Organic Chemistry and Chemical Biology, CMP/LiFF/ZAFES, Siesmayerstr. 70, 60323 Frankfurt am Main (Germany), Fax: (+49) 69‐798‐24880
| | - Kerstin Sander
- Goethe‐University, Institute of Pharmaceutical Chemistry, CMP/LiFF/ZAFES, Max‐von‐Laue‐Str. 9, 60438 Frankfurt am Main (Germany)
| | - Heiko Zettl
- Goethe‐University, Institute of Pharmaceutical Chemistry, LiFF/ZAFES, Max‐von‐Laue‐Str. 9, 60438 Frankfurt am Main (Germany)
| | - Yusuf Tanrikulu
- Goethe‐University, Institute of Organic Chemistry and Chemical Biology, CMP/LiFF/ZAFES, Siesmayerstr. 70, 60323 Frankfurt am Main (Germany), Fax: (+49) 69‐798‐24880
| | - Oliver Rau
- Goethe‐University, Institute of Pharmaceutical Chemistry, LiFF/ZAFES, Max‐von‐Laue‐Str. 9, 60438 Frankfurt am Main (Germany)
| | - Petra Schneider
- Schneider Consulting GbR, George‐C.‐Marshall Ring 33, 61440 Oberursel (Germany)
| | - Manfred Schubert‐Zsilavecz
- Goethe‐University, Institute of Pharmaceutical Chemistry, LiFF/ZAFES, Max‐von‐Laue‐Str. 9, 60438 Frankfurt am Main (Germany)
| | - Holger Stark
- Goethe‐University, Institute of Pharmaceutical Chemistry, CMP/LiFF/ZAFES, Max‐von‐Laue‐Str. 9, 60438 Frankfurt am Main (Germany)
| | - Gisbert Schneider
- Goethe‐University, Institute of Organic Chemistry and Chemical Biology, CMP/LiFF/ZAFES, Siesmayerstr. 70, 60323 Frankfurt am Main (Germany), Fax: (+49) 69‐798‐24880
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Schneider G, Hartenfeller M, Reutlinger M, Tanrikulu Y, Proschak E, Schneider P. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol 2009; 27:18-26. [DOI: 10.1016/j.tibtech.2008.09.005] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2008] [Revised: 09/14/2008] [Accepted: 09/17/2008] [Indexed: 11/30/2022]
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40
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Hartenfeller M, Proschak E, Schüller A, Schneider G. Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization. Chem Biol Drug Des 2008; 72:16-26. [PMID: 18564216 DOI: 10.1111/j.1747-0285.2008.00672.x] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.
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Affiliation(s)
- Markus Hartenfeller
- Institute of Organic Chemistry and Chemical Biology (ZAFES, CMP), Goethe University, Siesmayerstr. 70, D-60323 Frankfurt a.M., Germany
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41
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Alig L, Alsenz J, Andjelkovic M, Bendels S, Bénardeau A, Bleicher K, Bourson A, David-Pierson P, Guba W, Hildbrand S, Kube D, Lübbers T, Mayweg AV, Narquizian R, Neidhart W, Nettekoven M, Plancher JM, Rocha C, Rogers-Evans M, Röver S, Schneider G, Taylor S, Waldmeier P. Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. J Med Chem 2008; 51:2115-27. [PMID: 18335976 DOI: 10.1021/jm701487t] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The application of the evolutionary fragment-based de novo design tool TOPology Assigning System (TOPAS), starting from a known CB1R (CB-1 receptor) ligand, followed by further refinement principles, including pharmacophore compliance, chemical tractability, and drug likeness, allowed the identification of benzodioxoles as a novel CB1R inverse agonist series. Extensive multidimensional optimization was rewarded by the identification of promising lead compounds, showing in vivo activity. These compounds reversed the CP-55940-induced hypothermia in Naval Medical Research Institute (NMRI) mice and reduced body-weight gain, as well as fat mass, in diet-induced obese Sprague-Dawley rats. Herein, we disclose the tools and strategies that were employed for rapid hit identification, synthesis and generation of structure-activity relationships, ultimately leading to the identification of (+)-[( R)-2-(2,4-dichloride-phenyl)-6-fluoro-2-(4-fluoro-phenyl)-benzo[1,3]dioxol-5-yl]-morpholin-4-yl-methanone ( R)-14g . Biochemical, pharmacokinetic, and pharmacodynamic characteristics of ( R)-14g are discussed.
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Affiliation(s)
- Leo Alig
- Pharmaceuticals Division, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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Ren Y, Chen G, Hu Z, Chen X, Yan B. Applying Novel Three-Dimensional Holographic Vector of Atomic Interaction Field to QSAR Studies of Artemisinin Derivatives. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200630167] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Schüller A, Suhartono M, Fechner U, Tanrikulu Y, Breitung S, Scheffer U, Göbel MW, Schneider G. The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA. J Comput Aided Mol Des 2007; 22:59-68. [PMID: 18064402 DOI: 10.1007/s10822-007-9157-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2007] [Accepted: 11/19/2007] [Indexed: 11/25/2022]
Abstract
Principles of fragment-based molecular design are presented and discussed in the context of de novo drug design. The underlying idea is to dissect known drug molecules in fragments by straightforward pseudo-retro-synthesis. The resulting building blocks are then used for automated assembly of new molecules. A particular question has been whether this approach is actually able to perform scaffold-hopping. A prospective case study illustrates the usefulness of fragment-based de novo design for finding new scaffolds. We were able to identify a novel ligand disrupting the interaction between the Tat peptide and TAR RNA, which is part of the human immunodeficiency virus (HIV-1) mRNA. Using a single template structure (acetylpromazine) as reference molecule and a topological pharmacophore descriptor (CATS), new chemotypes were automatically generated by our de novo design software Flux. Flux features an evolutionary algorithm for fragment-based compound assembly and optimization. Pharmacophore superimposition and docking into the target RNA suggest perfect matching between the template molecule and the designed compound. Chemical synthesis was straightforward, and bioactivity of the designed molecule was confirmed in a FRET assay. This study demonstrates the practicability of de novo design to generating RNA ligands containing novel molecular scaffolds.
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Affiliation(s)
- Andreas Schüller
- Institute of Organic Chemistry and Chemical Biology, Johann Wolfgang Goethe-University, Max-von-Laue-Strasse 7, Chair for Chem- and Bioinformatics Siesmayerstr. 70, 60323 Frankfurt am Main, Germany
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Affiliation(s)
- Steffen Renner
- Institute of Organic Chemistry & Chemical Biology, Johann Wolfgang Goethe University, Siesmayerstrasse 70, 60323 Frankfurt, Germany
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45
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Alonso H, Bliznyuk AA, Gready JE. Combining docking and molecular dynamic simulations in drug design. Med Res Rev 2006; 26:531-68. [PMID: 16758486 DOI: 10.1002/med.20067] [Citation(s) in RCA: 438] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.
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Affiliation(s)
- Hernán Alonso
- Computational Proteomics Group, John Curtin School of Medical Research, The Australian National University, Canberra ACT 0200, Australia
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46
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Weber L. Chemistry for chemical genomics. Methods Mol Biol 2005; 310:11-24. [PMID: 16350944 DOI: 10.1007/978-1-59259-948-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
New methods and strategies have been developed to design and use small molecules that allow the functional dissection of molecular pathways, cells, and organisms by selective small-molecule ligands or modulators. In this overview, we are focusing on diversity aspects, design methods, and chemical synthesis strategies for the application of small molecules as tools for chemical genomics. Examples for different successful chemical-genomics strategies include the selection of diverse drug-like molecules, target family-focused compound libraries, natural-product chemistry, and diversity-oriented synthesis.
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47
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A De Novo Designed Inhibitor ofD-Ala-D-Ala Ligase fromE. coli. Angew Chem Int Ed Engl 2005. [DOI: 10.1002/ange.200501662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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48
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Besong GE, Bostock JM, Stubbings W, Chopra I, Roper DI, Lloyd AJ, Fishwick CWG, Johnson AP. A De Novo Designed Inhibitor ofD-Ala-D-Ala Ligase fromE. coli. Angew Chem Int Ed Engl 2005; 44:6403-6. [PMID: 16158456 DOI: 10.1002/anie.200501662] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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49
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Brendel J, Peukert S. Blockers of the Kv1.5 channel for the treatment of atrial arrhythmias. Expert Opin Ther Pat 2005. [DOI: 10.1517/13543776.12.11.1589] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Abstract
The aim of scaffold hopping is to discover structurally novel compounds starting from known active compounds by modifying the central core structure of the molecule. Scaffold hopping is a central task of modern medicinal chemistry requiring a multitude of techniques, which are discussed in this article. Their application has led to several molecules with chemically completely different core structures, and yet binding to the same receptor. Computational approaches for scaffold hopping highlight the challenges of the field that are still unsolved.:
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Affiliation(s)
- Hans-Joachim Böhm
- Molecular Structure and Design, Pharmaceuticals Division, F. Hoffmann-La Roche AG, PRBD-CS, Building 092/3.56B, CH-4070 Basel, Switzerland
| | - Alexander Flohr
- Molecular Structure and Design, Pharmaceuticals Division, F. Hoffmann-La Roche AG, PRBD-CS, Building 092/3.56B, CH-4070 Basel, Switzerland
| | - Martin Stahl
- Molecular Structure and Design, Pharmaceuticals Division, F. Hoffmann-La Roche AG, PRBD-CS, Building 092/3.56B, CH-4070 Basel, Switzerland.
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