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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [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] [Received: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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2
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Patel H, Ihlenfeldt WD, Judson PN, Moroz YS, Pevzner Y, Peach ML, Delannée V, Tarasova NI, Nicklaus MC. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci Data 2020; 7:384. [PMID: 33177514 PMCID: PMC7658252 DOI: 10.1038/s41597-020-00727-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023] Open
Abstract
We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.
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Affiliation(s)
- Hitesh Patel
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | | | - Philip N Judson
- Heather Lea, Bland Hill, Norwood, Harrogate, HG3 1TE, England
| | - Yurii S Moroz
- Enamine Ltd, 78 Chervonotkatska Street, Suite 1, Kyiv, 02094, Ukraine and Chemspace LLC, 85 Chervonotkatska Street, Suite 1, Kyiv, 02094, Ukraine
| | - Yuri Pevzner
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
- AbbVie, Inc., North Chicago, IL, 60064, USA
| | - Megan L Peach
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Victorien Delannée
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Nadya I Tarasova
- Synthetic Biologics and Drug Discovery Group, Laboratory of Cancer Immunometabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA.
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3
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Spiegel JO, Durrant JD. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J Cheminform 2020; 12:25. [PMID: 33431021 PMCID: PMC7165399 DOI: 10.1186/s13321-020-00429-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 03/31/2020] [Indexed: 02/06/2023] Open
Abstract
We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.![]()
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Affiliation(s)
- Jacob O Spiegel
- Department of Biological Sciences, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Jacob D Durrant
- Department of Biological Sciences, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
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5
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Wei J, Duvenaud D, Aspuru-Guzik A. Neural Networks for the Prediction of Organic Chemistry Reactions. ACS CENTRAL SCIENCE 2016; 2:725-732. [PMID: 27800555 PMCID: PMC5084081 DOI: 10.1021/acscentsci.6b00219] [Citation(s) in RCA: 227] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Indexed: 05/18/2023]
Abstract
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.
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Affiliation(s)
- Jennifer
N. Wei
- Department
of Chemistry and Chemical Biology, Harvard
University, Cambridge, Massachusetts 02138, United States
| | - David Duvenaud
- Department
of Computer Science, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Alán Aspuru-Guzik
- Department
of Chemistry and Chemical Biology, Harvard
University, Cambridge, Massachusetts 02138, United States
- E-mail:
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6
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Sushko Y, Novotarskyi S, Körner R, Vogt J, Abdelaziz A, Tetko IV. Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process. J Cheminform 2014; 6:48. [PMID: 25544551 PMCID: PMC4272757 DOI: 10.1186/s13321-014-0048-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Accepted: 11/07/2014] [Indexed: 11/24/2022] Open
Abstract
Background QSAR is an established and powerful method for cheap in silico assessment of physicochemical properties and biological activities of chemical compounds. However, QSAR models are rather complex mathematical constructs that cannot easily be interpreted. Medicinal chemists would benefit from practical guidance regarding which molecules to synthesize. Another possible approach is analysis of pairs of very similar molecules, so-called matched molecular pairs (MMPs). Such an approach allows identification of molecular transformations that affect particular activities (e.g. toxicity). In contrast to QSAR, chemical interpretation of these transformations is straightforward. Furthermore, such transformations can give medicinal chemists useful hints for the hit-to-lead optimization process. Results The current study suggests a combination of QSAR and MMP approaches by finding MMP transformations based on QSAR predictions for large chemical datasets. The study shows that such an approach, referred to as prediction-driven MMP analysis, is a useful tool for medicinal chemists, allowing identification of large numbers of “interesting” transformations that can be used to drive the molecular optimization process. All the methodological developments have been implemented as software products available online as part of OCHEM (http://ochem.eu/). Conclusions The prediction-driven MMPs methodology was exemplified by two use cases: modelling of aquatic toxicity and CYP3A4 inhibition. This approach helped us to interpret QSAR models and allowed identification of a number of “significant” molecular transformations that affect the desired properties. This can facilitate drug design as a part of molecular optimization process. Molecular matched pairs and transformation graphs facilitate interpretable molecular optimisation process. ![]()
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Affiliation(s)
- Yurii Sushko
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich Germany
| | | | - Robert Körner
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich Germany
| | - Joachim Vogt
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich Germany
| | - Ahmed Abdelaziz
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich Germany
| | - Igor V Tetko
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich Germany ; Helmholtz-Zentrum München - German Research Centre for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany ; A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya St. 18, 420008 Kazan, Russia
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7
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Peng Z. Very large virtual compound spaces: construction, storage and utility in drug discovery. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 10:e387-e394. [PMID: 24050135 DOI: 10.1016/j.ddtec.2013.01.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.
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Hartenfeller M, Eberle M, Meier P, Nieto-Oberhuber C, Altmann KH, Schneider G, Jacoby E, Renner S. A collection of robust organic synthesis reactions for in silico molecule design. J Chem Inf Model 2011; 51:3093-8. [PMID: 22077721 DOI: 10.1021/ci200379p] [Citation(s) in RCA: 86] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A focused collection of organic synthesis reactions for computer-based molecule construction is presented. It is inspired by real-world chemistry and has been compiled in close collaboration with medicinal chemists to achieve high practical relevance. Virtual molecules assembled from existing starting material connected by these reactions are supposed to have an enhanced chance to be amenable to real chemical synthesis. About 50% of the reactions in the dataset are ring-forming reactions, which fosters the assembly of novel ring systems and innovative chemotypes. A comparison with a recent survey of the reactions used in early drug discovery revealed considerable overlaps with the collection presented here. The dataset is available encoded as computer-readable Reaction SMARTS expressions from the Supporting Information presented for this paper.
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Affiliation(s)
- Markus Hartenfeller
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland.
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9
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Boehm M. Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries. ACTA ACUST UNITED AC 2011. [DOI: 10.1002/9783527633326.ch1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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10
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Computational medicinal chemistry in fragment-based drug discovery: what, how and when. Future Med Chem 2011; 3:95-134. [DOI: 10.4155/fmc.10.277] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.
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11
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Nowak G, Fic G. Search for complexity generating chemical transformations by combining connectivity analysis and cascade transformation patterns. J Chem Inf Model 2010; 50:1369-77. [PMID: 20681604 DOI: 10.1021/ci100146n] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Retrosynthetic analysis involved in a backward search for strategic disconnections is still the most powerful strategy, recently advanced by topology-based complexity estimation, for discovering the shortest sequences of transformations and chemical synthesis planning. Therein, we propose an alternative strategy that combines backward and forward search embodied within a mathematical model of generating chemical transformations. The backward reasoning involves a new concept of the strategic bond tree for alternative multibond disconnections of a target molecule. In the forward direction, each combination of the resulted structural fragments is examined for reconstruction of the target structure by means of biomimetic transformation patterns that describe one-pot multibond forming reactions. The algorithm has been implemented into the CSB system, and its performance is illustrated by examples of published complex molecule syntheses for comparison and analysis. This paper describes the strategy for discovering the shortest synthetic pathways based on the multibond forming cascade transformations for application in synthesis design and generating synthetically accessible product libraries.
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Affiliation(s)
- Grazyna Nowak
- Departments of Physical Chemistry and Computer Chemistry, Faculty of Chemistry, Rzeszow University of Technology, Al. Powstancow Warszawy 6, 35-959 Rzeszow, Poland.
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12
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Patel H, Bodkin MJ, Chen B, Gillet VJ. Knowledge-based approach to de novo design using reaction vectors. J Chem Inf Model 2009; 49:1163-84. [PMID: 19382767 DOI: 10.1021/ci800413m] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on reaction vectors which represent the structural changes that take place at the reaction center along with the environment in which the reaction occurs. The reaction vectors are derived automatically from a database of reactions which is not restricted by size or reaction complexity. A structure generation algorithm has been developed whereby reaction vectors can be applied to previously unseen starting materials in order to suggest novel syntheses. The approach has been implemented in KNIME and is validated by reproducing known synthetic routes. We then present applications of the method in different drug design scenarios including lead optimization and library enumeration. The method offers great potential for capturing and using the growing body of data on reactions that is becoming available through electronic laboratory notebooks.
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Affiliation(s)
- Hina Patel
- Department of Information Studies, University of Sheffield, Regent Court, Sheffield S1 4DP, UK
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13
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Lessel U, Wellenzohn B, Lilienthal M, Claussen H. Searching Fragment Spaces with Feature Trees. J Chem Inf Model 2009; 49:270-9. [DOI: 10.1021/ci800272a] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Uta Lessel
- Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany, and BioSolveIT, An der Ziegelei 75, 53757 St. Augustin, Germany
| | - Bernd Wellenzohn
- Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany, and BioSolveIT, An der Ziegelei 75, 53757 St. Augustin, Germany
| | - Markus Lilienthal
- Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany, and BioSolveIT, An der Ziegelei 75, 53757 St. Augustin, Germany
| | - Holger Claussen
- Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany, and BioSolveIT, An der Ziegelei 75, 53757 St. Augustin, Germany
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14
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Hertkorn N, Frommberger M, Witt M, Koch BP, Schmitt-Kopplin P, Perdue EM. Natural Organic Matter and the Event Horizon of Mass Spectrometry. Anal Chem 2008; 80:8908-19. [DOI: 10.1021/ac800464g] [Citation(s) in RCA: 308] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- N. Hertkorn
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - M. Frommberger
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - M. Witt
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - B. P. Koch
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Ph. Schmitt-Kopplin
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - E. M. Perdue
- Institute of Ecological Chemistry, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, Bruker Daltonics, Fahrenheitstrasse 4, D-28359 Bremen, Germany, Alfred-Wegener-Institut für Polar- and Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332
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15
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McGregor MJ. A Pharmacophore Map of Small Molecule Protein Kinase Inhibitors. J Chem Inf Model 2007; 47:2374-82. [DOI: 10.1021/ci700244t] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Malcolm J. McGregor
- EMD Serono Research Institute, One Technology Place, Rockland, Massachusetts 02370
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16
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Abstract
The history of chemoinformatics is reviewed in a decade-by-decade manner from the 1940s to the present. The focus is placed on four traditional research areas: chemical database systems, computer-assisted structure elucidation systems, computer-assisted synthesis design systems, and 3D structure builders. Considering the fact that computer technology has been one of the major driving forces of the development of chemoinformatics, each section will start from a brief description of the new advances in computer technology of each decade. The summary and future prospects are given in the last section.
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