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Schaub J, Zander J, Zielesny A, Steinbeck C. Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK). J Cheminform 2022; 14:79. [PMID: 36357931 PMCID: PMC9650898 DOI: 10.1186/s13321-022-00656-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 10/30/2022] [Indexed: 11/12/2022] Open
Abstract
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
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Affiliation(s)
- Jonas Schaub
- grid.9613.d0000 0001 1939 2794Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessing Strasse 8, 07743 Jena, Germany
| | - Julian Zander
- grid.454254.60000 0004 0647 4362Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Achim Zielesny
- grid.454254.60000 0004 0647 4362Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Christoph Steinbeck
- grid.9613.d0000 0001 1939 2794Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessing Strasse 8, 07743 Jena, Germany
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2
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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3
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Medina-Franco JL, Naveja JJ, López-López E. Reaching for the bright StARs in chemical space. Drug Discov Today 2019; 24:2162-2169. [PMID: 31557448 DOI: 10.1016/j.drudis.2019.09.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 09/10/2019] [Accepted: 09/17/2019] [Indexed: 02/07/2023]
Abstract
Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.
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Affiliation(s)
- José L Medina-Franco
- Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico.
| | - J Jesús Naveja
- Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico; PECEM, School of Medicine, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Edgar López-López
- Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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Capoci IRG, Faria DR, Sakita KM, Rodrigues-Vendramini FAV, Bonfim-Mendonça PDS, Becker TCA, Kioshima ÉS, Svidzinski TIE, Maigret B. Repurposing approach identifies new treatment options for invasive fungal disease. Bioorg Chem 2018; 84:87-97. [PMID: 30496872 DOI: 10.1016/j.bioorg.2018.11.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/17/2018] [Accepted: 11/16/2018] [Indexed: 12/29/2022]
Abstract
Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.
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Affiliation(s)
| | - Daniella Renata Faria
- Department of Clinical Analysis and Biomedicine, State University of Maringá, Maringá, Paraná, Brazil
| | - Karina Mayumi Sakita
- Department of Clinical Analysis and Biomedicine, State University of Maringá, Maringá, Paraná, Brazil
| | | | | | | | - Érika Seki Kioshima
- Department of Clinical Analysis and Biomedicine, State University of Maringá, Maringá, Paraná, Brazil
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5
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Nakagawa T, Miyao T, Funatsu K. Identification of Bioactive Scaffolds Based on QSAR Models. Mol Inform 2017; 37. [DOI: 10.1002/minf.201700103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 10/02/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Tomoki Nakagawa
- Department of Chemical System Engineering, School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
| | - Tomoyuki Miyao
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Dahlmannstr. 2 D-53113 Bonn Germany
| | - Kimito Funatsu
- Department of Chemical System Engineering, School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
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Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 2017; 9:33. [PMID: 29086040 PMCID: PMC5461230 DOI: 10.1186/s13321-017-0220-4] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 05/16/2017] [Indexed: 12/15/2022] Open
Abstract
Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.CDK 2.0 provides new features and improved performance ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0220-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands.
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Arvid Berg
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Lars Carlsson
- AstraZeneca, Innovative Medicines & Early Development, Quantitative Biology, Möndal, Sweden
| | | | - Stefan Kuhn
- Department of Informatics, University of Leicester, Leicester, UK
| | - Tomáš Pluskal
- Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA, 02142, USA
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | | | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD, 20850, USA
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Lessingstr. 8, 07743, Jena, Germany
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Schäfer T, Kriege N, Humbeck L, Klein K, Koch O, Mutzel P. Scaffold Hunter: a comprehensive visual analytics framework for drug discovery. J Cheminform 2017; 9:28. [PMID: 29086162 PMCID: PMC5425364 DOI: 10.1186/s13321-017-0213-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 04/10/2017] [Indexed: 01/31/2023] Open
Abstract
The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold Hunter is a flexible visual analytics framework for the analysis of chemical compound data and combines techniques from several fields such as data mining and information visualization. The framework allows analyzing high-dimensional chemical compound data in an interactive fashion, combining intuitive visualizations with automated analysis methods including versatile clustering methods. Originally designed to analyze the scaffold tree, Scaffold Hunter is continuously revised and extended. We describe recent extensions that significantly increase the applicability for a variety of tasks.
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Affiliation(s)
- Till Schäfer
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany
| | - Nils Kriege
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany
| | - Lina Humbeck
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, Dortmund, 44227, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Universitaetsstrasse 10, Konstanz, 78464, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, Dortmund, 44227, Germany.
| | - Petra Mutzel
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany.
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Verma K, Kannan K, V S, R S, V K, K R. Exploring β-Tubulin Inhibitors from Plant Origin using Computational Approach. PHYTOCHEMICAL ANALYSIS : PCA 2017; 28:230-241. [PMID: 28008675 DOI: 10.1002/pca.2665] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 10/24/2016] [Accepted: 10/24/2016] [Indexed: 05/28/2023]
Abstract
INTRODUCTION β-Tubulin is an important target for the binding of anti-cancer drugs, in particular, paclitaxel (taxol), vinblastine and epothilone. However, mutations in β-tubulin structure give resistance to chemotherapeutic agents. Notably, mutations at R306C, F270 V, L217R, L228F, A185T and A248V positions in β-tubulin give high resistance for paclitaxel binding. OBJECTIVE To discover novel inhibitors of β-tubulin from natural sources, particularly alkaloids, using a virtual screening approach. METHODOLOGY A virtual screening approach was employed to find potent lead molecules from the Naturally-occurring Plant-based Anti-cancer Compound-activity Target (NPACT) database. Alkaloids have great potential to be anti-cancer agents. Therefore, we have screened all alkaloids from a total of 1574 molecules from the NPACT database for our study. Initially, Molinspiration and DataWarrior programs were utilised to calculate pharmacokinetics and toxicity risks of the alkaloids, respectively. Subsequently, AutoDock algorithm was employed to understand the binding efficiency of alkaloids against β-tubulin. The binding affinity of the docked complex was confirmed by means of an intermolecular interaction study. Moreover, oral toxicity was predicted by using ProTox program. Further, metabolising capacity of drugs was studied by using SmartCYP software. Additionally, scaffold analysis was done with the help of scaffold trees and dendrograms, providing knowledge about the building blocks for parent-compound synthesis. RESULTS Overall, the results of our computational analysis indicate that isostrychnine, obtained from Strychnosnux-vomica, satisfies pharmacokinetic and bioavailability properties, binds efficiently with β-tubulin. Thus, it could be a promising lead for the treatment of paclitaxel resistant cancer types. CONCLUSION This is the first observation of inhibitory activity of isostrychnine against β-tubulin and warrants further experimental investigation. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Kanika Verma
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Kaavya Kannan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Shanthi V
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Sethumadhavan R
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
| | - Karthick V
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Ramanathan K
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India
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Humbeck L, Koch O. What Can We Learn from Bioactivity Data? Chemoinformatics Tools and Applications in Chemical Biology Research. ACS Chem Biol 2017; 12:23-35. [PMID: 27779378 DOI: 10.1021/acschembio.6b00706] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ever increasing bioactivity data that are produced nowadays allow exhaustive data mining and knowledge discovery approaches that change chemical biology research. A wealth of chemoinformatics tools, web services, and applications therefore exists that supports a careful evaluation and analysis of experimental data to draw conclusions that can influence the further development of chemical probes and potential lead structures. This review focuses on open-source approaches that can be handled by scientists who are not familiar with computational methods having no expert knowledge in chemoinformatics and modeling. Our aim is to present an easily manageable toolbox for support of every day laboratory work. This includes, among other things, the available bioactivity and related molecule databases as well as tools to handle and analyze in-house data.
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Affiliation(s)
- Lina Humbeck
- Faculty
of Chemistry and
Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Oliver Koch
- Faculty
of Chemistry and
Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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Kringelum J, Kjaerulff SK, Brunak S, Lund O, Oprea TI, Taboureau O. ChemProt-3.0: a global chemical biology diseases mapping. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:bav123. [PMID: 26876982 PMCID: PMC4752971 DOI: 10.1093/database/bav123] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 12/08/2015] [Indexed: 12/16/2022]
Abstract
ChemProt is a publicly available compilation of chemical-protein-disease annotation resources that enables the study of systems pharmacology for a small molecule across multiple layers of complexity from molecular to clinical levels. In this third version, ChemProt has been updated to more than 1.7 million compounds with 7.8 million bioactivity measurements for 19 504 proteins. Here, we report the implementation of global pharmacological heatmap, supporting a user-friendly navigation of chemogenomics space. This facilitates the visualization and selection of chemicals that share similar structural properties. In addition, the user has the possibility to search by compound, target, pathway, disease and clinical effect. Genetic variations associated to target proteins were integrated, making it possible to plan pharmacogenetic studies and to suggest human response variability to drug. Finally, Quantitative Structure–Activity Relationship models for 850 proteins having sufficient data were implemented, enabling secondary pharmacological profiling predictions from molecular structure. Database URL: http://potentia.cbs.dtu.dk/ChemProt/
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Affiliation(s)
- Jens Kringelum
- Department of Systems Biology, Center for Biological Sequence Analysis, buildin 208, kemitorvet, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Sonny Kim Kjaerulff
- Department of Systems Biology, Center for Biological Sequence Analysis, buildin 208, kemitorvet, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Søren Brunak
- Department of Disease Systems Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation, Center for Protein Research, University of Copenhagen, Blegdamsvej 3A, DK-2200, Copenhagen, Denmark
| | - Ole Lund
- Department of Systems Biology, Center for Biological Sequence Analysis, buildin 208, kemitorvet, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Tudor I Oprea
- Department of Systems Biology, Center for Biological Sequence Analysis, buildin 208, kemitorvet, Technical University of Denmark, DK-2800 Lyngby, Denmark Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, MSC09 5025, Albuquerque 87181, New Mexico, United States of America
| | - Olivier Taboureau
- Department of Systems Biology, Center for Biological Sequence Analysis, buildin 208, kemitorvet, Technical University of Denmark, DK-2800 Lyngby, Denmark INSERM, UMRS-973, MTi, Universite Paris Diderot, 35 rue Helene Brion, 75205 Paris Cedex 13, Sorbonne Paris Cite, France
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Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS. Progress in visual representations of chemical space. Expert Opin Drug Discov 2015; 10:959-73. [DOI: 10.1517/17460441.2015.1060216] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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12
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Molecular modeling and identification of novel glucokinase activators through stepwise virtual screening. J Mol Graph Model 2015; 57:122-30. [DOI: 10.1016/j.jmgm.2015.01.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 01/28/2015] [Accepted: 01/29/2015] [Indexed: 11/17/2022]
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Abstract
In this article strategies for the design and synthesis of natural product analogues are summarized and illustrated with some selected examples.
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Affiliation(s)
- Martin E. Maier
- Institut für Organische Chemie
- Eberhard Karls Universität Tübingen
- 72076 Tübingen
- Germany
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14
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Abstract
Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.
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