1
|
Abstract
INTRODUCTION Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.
Collapse
Affiliation(s)
- Martin Vogt
- a Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Bonn , Germany
| |
Collapse
|
2
|
Naveja JJ, Norinder U, Mucs D, López-López E, Medina-Franco JL. Chemical space, diversity and activity landscape analysis of estrogen receptor binders. RSC Adv 2018; 8:38229-38237. [PMID: 35559115 PMCID: PMC9089822 DOI: 10.1039/c8ra07604a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/05/2018] [Indexed: 11/21/2022] Open
Abstract
Understanding the structure–activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models. Global diversity and activity landscape analysis of endocrine-disrupting chemicals identifies activity cliffs that are rationalized at the structure level.![]()
Collapse
Affiliation(s)
- J. Jesús Naveja
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Ulf Norinder
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Daniel Mucs
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Edgar López-López
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Josė L. Medina-Franco
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| |
Collapse
|
3
|
Abstract
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.
Collapse
|
4
|
From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets. J Comput Aided Mol Des 2017; 31:961-977. [DOI: 10.1007/s10822-017-0070-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 09/21/2017] [Indexed: 01/18/2023]
|
5
|
Tibbetts KM, Feng XJ, Rabitz H. Exploring experimental fitness landscapes for chemical synthesis and property optimization. Phys Chem Chem Phys 2017; 19:4266-4287. [DOI: 10.1039/c6cp06187g] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The topology of experimental fitness landscapes for chemical optimization objectives is assessed through svr-based HDMR modeling.
Collapse
|
6
|
|
7
|
Button AL, Hiss JA, Schneider P, Schneider G. Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction. Mol Inform 2016; 36. [PMID: 27643811 DOI: 10.1002/minf.201600110] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 08/27/2016] [Indexed: 11/10/2022]
Abstract
Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.
Collapse
Affiliation(s)
- Alexander L Button
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Petra Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland.,inSili.com LLC, Segantinisteig 3, CH-, 8049, Zurich, Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| |
Collapse
|
8
|
Tetko IV, Engkvist O, Koch U, Reymond JL, Chen H. BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry. Mol Inform 2016; 35:615-621. [PMID: 27464907 PMCID: PMC5129546 DOI: 10.1002/minf.201600073] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/06/2016] [Indexed: 01/19/2023]
Abstract
The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.
Collapse
Affiliation(s)
- Igor V Tetko
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.,BIGCHEM GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany
| | - Ola Engkvist
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
| | - Uwe Koch
- Lead Discovery Center GmbH, Otto-Hahn Strasse 15, Dortmund, 44227, Germany
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Hongming Chen
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
| |
Collapse
|
9
|
Awale M, Reymond JL. Web-based 3D-visualization of the DrugBank chemical space. J Cheminform 2016; 8:25. [PMID: 27148409 PMCID: PMC4855437 DOI: 10.1186/s13321-016-0138-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 04/27/2016] [Indexed: 12/14/2022] Open
Abstract
Background Similarly to the periodic table for elements, chemical space offers an organizing principle for representing the diversity of organic molecules, usually in the form of multi-dimensional property spaces that are subjected to dimensionality reduction methods to obtain 3D-spaces or 2D-maps suitable for visual inspection. Unfortunately, tools to look at chemical space on the internet are currently very limited. Results Herein we present webDrugCS, a web application freely available at www.gdb.unibe.ch to visualize DrugBank (www.drugbank.ca, containing over 6000 investigational and approved drugs) in five different property spaces. WebDrugCS displays 3D-clouds of color-coded grid points representing molecules, whose structural formula is displayed on mouse over with an option to link to the corresponding molecule page at the DrugBank website. The 3D-clouds are obtained by principal component analysis of high dimensional property spaces describing constitution and topology (42D molecular quantum numbers MQN), structural features (34D SMILES fingerprint SMIfp), molecular shape (20D atom pair fingerprint APfp), pharmacophores (55D atom category extended atom pair fingerprint Xfp) and substructures (1024D binary substructure fingerprint Sfp). User defined molecules can be uploaded as SMILES lists and displayed together with DrugBank. In contrast to 2D-maps where many compounds fold onto each other, these 3D-spaces have a comparable resolution to their parent high-dimensional chemical space. Conclusion To the best of our knowledge webDrugCS is the first publicly available web tool for interactive visualization and exploration of the DrugBank chemical space in 3D. WebDrugCS works on computers, tablets and phones, and facilitates the visual exploration of DrugBank to rapidly learn about the structural diversity of small molecule drugs.webDrugCS visualization of DrugBank projected in 3D MQN space color-coded by ring count, with pointer showing the drug 5-fluorouracil. ![]()
Collapse
Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| |
Collapse
|
10
|
Dimova D, Bajorath J. Advances in Activity Cliff Research. Mol Inform 2016; 35:181-91. [PMID: 27492084 DOI: 10.1002/minf.201600023] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 02/29/2016] [Indexed: 12/29/2022]
Abstract
Activity cliffs, i.e. similar compounds with large potency differences, are of interest from a chemical and informatics viewpoint; as a source of structure-activity relationship information, for compound optimization, and activity prediction. Herein, recent highlights of activity cliff research are discussed including studies that have further extended our understanding of activity cliffs, yielded unprecedented insights, or paved the way for practical applications.
Collapse
Affiliation(s)
- Dilyana Dimova
- Department of Life Science Informatics, Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn (Germany), Tel: +49-228-2699-306, Fax: +49-228-2699-341
| | - Jürgen Bajorath
- Department of Life Science Informatics, Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn (Germany), Tel: +49-228-2699-306, Fax: +49-228-2699-341.
| |
Collapse
|
11
|
Bieler M, Reutlinger M, Rodrigues T, Schneider P, Kriegl JM, Schneider G. Designing Multi-target Compound Libraries with Gaussian Process Models. Mol Inform 2016; 35:192-8. [PMID: 27492085 DOI: 10.1002/minf.201501012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 02/02/2016] [Indexed: 11/07/2022]
Abstract
We present the application of machine learning models to selecting G protein-coupled receptor (GPCR)-focused compound libraries. The library design process was realized by ant colony optimization. A proprietary Boehringer-Ingelheim reference set consisting of 3519 compounds tested in dose-response assays at 11 GPCR targets served as training data for machine learning and activity prediction. We compared the usability of the proprietary data with a public data set from ChEMBL. Gaussian process models were trained to prioritize compounds from a virtual combinatorial library. We obtained meaningful models for three of the targets (5-HT2c , MCH, A1), which were experimentally confirmed for 12 of 15 selected and synthesized or purchased compounds. Overall, the models trained on the public data predicted the observed assay results more accurately. The results of this study motivate the use of Gaussian process regression on public data for virtual screening and target-focused compound library design.
Collapse
Affiliation(s)
- Michael Bieler
- Boehringer Ingelheim Pharma GmbH & Co. KG, Lead Identification and Optimization Support, Birkendorfer Strasse 65, 88397 Biberach an der Riss.
| | - Michael Reutlinger
- Swiss Federal Institute of Technology (ETH) Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| | - Tiago Rodrigues
- Swiss Federal Institute of Technology (ETH) Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| | - Petra Schneider
- Swiss Federal Institute of Technology (ETH) Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
| | - Jan M Kriegl
- Boehringer Ingelheim Pharma GmbH & Co. KG, Lead Identification and Optimization Support, Birkendorfer Strasse 65, 88397 Biberach an der Riss
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH) Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.
| |
Collapse
|
12
|
Abstract
Shown is a section of an SAR network. Nodes represent compounds and are colored by potency and edges indicate pair-wise similarity relationships.
Collapse
Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität Bonn
- D-53113 Bonn
| | - Jürgen Bajorath
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität Bonn
- D-53113 Bonn
| |
Collapse
|
13
|
Rodrigues T, Reker D, Welin M, Caldera M, Brunner C, Gabernet G, Schneider P, Walse B, Schneider G. De Novo Fragment Design for Drug Discovery and Chemical Biology. Angew Chem Int Ed Engl 2015; 54:15079-83. [DOI: 10.1002/anie.201508055] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Indexed: 01/08/2023]
|
14
|
Rodrigues T, Reker D, Welin M, Caldera M, Brunner C, Gabernet G, Schneider P, Walse B, Schneider G. De-novo-Fragmententwurf für die Wirkstoffforschung und chemische Biologie. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201508055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
15
|
Awale M, Reymond JL. Similarity Mapplet: Interactive Visualization of the Directory of Useful Decoys and ChEMBL in High Dimensional Chemical Spaces. J Chem Inf Model 2015. [PMID: 26207526 DOI: 10.1021/acs.jcim.5b00182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An Internet portal accessible at www.gdb.unibe.ch has been set up to automatically generate color-coded similarity maps of the ChEMBL database in relation to up to two sets of active compounds taken from the enhanced Directory of Useful Decoys (eDUD), a random set of molecules, or up to two sets of user-defined reference molecules. These maps visualize the relationships between the selected compounds and ChEMBL in six different high dimensional chemical spaces, namely MQN (42-D molecular quantum numbers), SMIfp (34-D SMILES fingerprint), APfp (20-D shape fingerprint), Xfp (55-D pharmacophore fingerprint), Sfp (1024-bit substructure fingerprint), and ECfp4 (1024-bit extended connectivity fingerprint). The maps are supplied in form of Java based desktop applications called "similarity mapplets" allowing interactive content browsing and linked to a "Multifingerprint Browser for ChEMBL" (also accessible directly at www.gdb.unibe.ch ) to perform nearest neighbor searches. One can obtain six similarity mapplets of ChEMBL relative to random reference compounds, 606 similarity mapplets relative to single eDUD active sets, 30,300 similarity mapplets relative to pairs of eDUD active sets, and any number of similarity mapplets relative to user-defined reference sets to help visualize the structural diversity of compound series in drug optimization projects and their relationship to other known bioactive compounds.
Collapse
Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| |
Collapse
|
16
|
de la Vega de León A, Kayastha S, Dimova D, Schultz T, Bajorath J. Visualization of multi-property landscapes for compound selection and optimization. J Comput Aided Mol Des 2015; 29:695-705. [DOI: 10.1007/s10822-015-9862-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 07/27/2015] [Indexed: 01/13/2023]
|
17
|
Reker D, Schneider G. Active-learning strategies in computer-assisted drug discovery. Drug Discov Today 2015; 20:458-65. [DOI: 10.1016/j.drudis.2014.12.004] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 11/13/2014] [Accepted: 12/02/2014] [Indexed: 12/20/2022]
|
18
|
Rodrigues T, Hauser N, Reker D, Reutlinger M, Wunderlin T, Hamon J, Koch G, Schneider G. Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angew Chem Int Ed Engl 2014; 54:1551-5. [PMID: 25475886 DOI: 10.1002/anie.201410201] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Revised: 10/30/2014] [Indexed: 11/10/2022]
Abstract
We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.
Collapse
Affiliation(s)
- Tiago Rodrigues
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland)
| | | | | | | | | | | | | | | |
Collapse
|
19
|
Rodrigues T, Hauser N, Reker D, Reutlinger M, Wunderlin T, Hamon J, Koch G, Schneider G. Multidimensional De Novo Design Reveals 5-HT2BReceptor-Selective Ligands. Angew Chem Int Ed Engl 2014. [DOI: 10.1002/ange.201410201] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
20
|
Mishima K, Kaneko H, Funatsu K. Development of a New De Novo Design Algorithm for Exploring Chemical Space. Mol Inform 2014; 33:779-89. [PMID: 27485424 DOI: 10.1002/minf.201400056] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2014] [Accepted: 07/29/2014] [Indexed: 01/10/2023]
Abstract
In the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical space defined by structural descriptors. New compounds close to areas where highly active compounds exist will show the same degree of activity. We have developed a new de novo design system to search a target area in chemical space. First, highly active compounds are manually selected as initial seeds. Then, the seeds are entered into our system, and structures slightly different from the seeds are generated and pooled. Next, seeds are selected from the new structure pool based on the distance from target coordinates on the map. To test the algorithm, we used two datasets of ligand binding affinity and showed that the proposed generator could produce diverse virtual compounds that had high activity in docking simulations.
Collapse
Affiliation(s)
- Kazuaki Mishima
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan tel:(+81) 03-5841-7751
| | - Hiromasa Kaneko
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan tel:(+81) 03-5841-7751
| | - Kimito Funatsu
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan tel:(+81) 03-5841-7751.
| |
Collapse
|
21
|
Schneider G. De novo design - hop(p)ing against hope. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e453-60. [PMID: 24451634 DOI: 10.1016/j.ddtec.2012.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Current trends in computational de novo design provide a fresh approach to 'scaffold-hopping' in drug discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.
Collapse
|
22
|
Abstract
The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.
Collapse
Affiliation(s)
- Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.
| |
Collapse
|
23
|
Ovchinnikova SI, Bykov AA, Tsivadze AY, Dyachkov EP, Kireeva NV. Supervised extensions of chemography approaches: case studies of chemical liabilities assessment. J Cheminform 2014; 6:20. [PMID: 24868246 PMCID: PMC4018504 DOI: 10.1186/1758-2946-6-20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 04/28/2014] [Indexed: 12/04/2022] Open
Abstract
Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model's applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.
Collapse
Affiliation(s)
- Svetlana I Ovchinnikova
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky pr-t 31-4, 119071 Moscow, Russia
- Moscow Institute of Physics and Technology, Institutsky per., 9, 141700 Dolgoprudny, Russia
| | - Arseniy A Bykov
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky pr-t 31-4, 119071 Moscow, Russia
- Moscow Institute of Physics and Technology, Institutsky per., 9, 141700 Dolgoprudny, Russia
| | - Aslan Yu Tsivadze
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky pr-t 31-4, 119071 Moscow, Russia
| | - Evgeny P Dyachkov
- Kurnakov Institute of General and Inorganic Chemistry RAS, Leninsky pr-t 31, 119071 Moscow, Russia
| | - Natalia V Kireeva
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Leninsky pr-t 31-4, 119071 Moscow, Russia
- Moscow Institute of Physics and Technology, Institutsky per., 9, 141700 Dolgoprudny, Russia
| |
Collapse
|
24
|
Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance-Based Versus Topology-Based Approaches. ChemMedChem 2014; 9:1047-59. [DOI: 10.1002/cmdc.201400027] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Indexed: 01/11/2023]
|
25
|
Reutlinger M, Rodrigues T, Schneider P, Schneider G. Mehrdimensionales De-novo-Moleküldesign durch adaptive Fragmentauswahl. Angew Chem Int Ed Engl 2014. [DOI: 10.1002/ange.201310864] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
26
|
Reutlinger M, Rodrigues T, Schneider P, Schneider G. Multi-objective molecular de novo design by adaptive fragment prioritization. Angew Chem Int Ed Engl 2014; 53:4244-8. [PMID: 24623390 DOI: 10.1002/anie.201310864] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2013] [Indexed: 11/11/2022]
Abstract
We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.
Collapse
Affiliation(s)
- Michael Reutlinger
- Eidgenössische Technische Hochschule (ETH), Departement Chemie und Angewandte Biowissenschaften, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | | | | | | |
Collapse
|
27
|
Machine learning estimates of natural product conformational energies. PLoS Comput Biol 2014; 10:e1003400. [PMID: 24453952 PMCID: PMC3894151 DOI: 10.1371/journal.pcbi.1003400] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Accepted: 10/10/2013] [Indexed: 11/19/2022] Open
Abstract
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. Molecular dynamics simulations provide insight into the dynamic behavior of molecules, e.g., into the adopted spatial arrangements of its atoms over time. Methods differ in the approximations they employ, resulting in a trade-off between accuracy and speed that ranges from highly accurate but expensive quantum mechanical calculations to fast but more inaccurate molecular mechanics force fields. Machine learning, a sub-discipline of artificial intelligence, provides algorithms that learn from data, that is, make predictions based on previously seen examples. By starting with a few expensive quantum mechanical calculations, training a machine learning algorithm on them, and then using the resulting model to carry out the molecular dynamics simulation, one can improve the accuracy/speed trade-off. We have developed and applied such a hybrid quantum mechanics/machine learning approach to Archazolid A, a natural product from the myxobacterium Archangium gephyra and a potent inhibitor of vacuolar-type ATPase. By dynamically refining our model over the course of the simulation, we achieve errors of less than 1 kcal/mol while saving over 40% of the quantum mechanical calculations. Our study demonstrates the feasibility of predictive machine learning models for the dynamics of structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of even larger biomolecular structures.
Collapse
|
28
|
Medina-Franco JL, Aguayo-Ortiz R. Progress in the Visualization and Mining of Chemical and Target Spaces. Mol Inform 2013; 32:942-53. [DOI: 10.1002/minf.201300041] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2013] [Accepted: 05/06/2013] [Indexed: 01/15/2023]
|
29
|
Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G. Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules. Mol Inform 2013; 32:133-138. [PMID: 23956801 PMCID: PMC3743170 DOI: 10.1002/minf.201200141] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 01/18/2013] [Indexed: 02/04/2023]
Affiliation(s)
- Michael Reutlinger
- ETH, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences Wolfgang-Pauli-Str. 10, CH-8093 Zurich, Switzerland fax: +41 44 633 13 79, tel: +41 44 633 74 38
| | | | | | | | | | | | | |
Collapse
|
30
|
|
31
|
Lounkine E, Kutchukian P, Petrone P, Davies JW, Glick M. Chemotography for multi-target SAR analysis in the context of biological pathways. Bioorg Med Chem 2012; 20:5416-27. [PMID: 22405595 DOI: 10.1016/j.bmc.2012.02.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2012] [Revised: 02/08/2012] [Accepted: 02/11/2012] [Indexed: 10/28/2022]
Abstract
The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.
Collapse
Affiliation(s)
- Eugen Lounkine
- Lead Discovery Informatics, Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, MA 02139, USA.
| | | | | | | | | |
Collapse
|
32
|
Reutlinger M, Schneider G. Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. J Mol Graph Model 2012; 34:108-17. [PMID: 22326864 DOI: 10.1016/j.jmgm.2011.12.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2011] [Revised: 12/13/2011] [Accepted: 12/14/2011] [Indexed: 01/29/2023]
Abstract
Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.
Collapse
Affiliation(s)
- Michael Reutlinger
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | | |
Collapse
|
33
|
Fjell CD, Hiss JA, Hancock REW, Schneider G. Designing antimicrobial peptides: form follows function. Nat Rev Drug Discov 2011; 11:37-51. [PMID: 22173434 DOI: 10.1038/nrd3591] [Citation(s) in RCA: 1344] [Impact Index Per Article: 103.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.
Collapse
Affiliation(s)
- Christopher D Fjell
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, 2259 Lower Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | | | | | | |
Collapse
|
34
|
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]
|