1
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Akgüller Ö, Balcı MA, Cioca G. Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships. Int J Mol Sci 2024; 25:6890. [PMID: 38999999 PMCID: PMC11240958 DOI: 10.3390/ijms25136890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.
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Affiliation(s)
- Ömer Akgüller
- Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey;
| | - Mehmet Ali Balcı
- Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey;
| | - Gabriela Cioca
- Preclinical Department, Faculty of Medicine, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania;
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2
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Xue X, Sun H, Yang M, Liu X, Hu HY, Deng Y, Wang X. Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective. Anal Chem 2023; 95:13733-13745. [PMID: 37688541 DOI: 10.1021/acs.analchem.3c02540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
Abstract
The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.
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Affiliation(s)
- Xi Xue
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Hanyu Sun
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Minjian Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Xue Liu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Hai-Yu Hu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaojian Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
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3
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Dost K, Pullar-Strecker Z, Brydon L, Zhang K, Hafner J, Riddle PJ, Wicker JS. Combatting over-specialization bias in growing chemical databases. J Cheminform 2023; 15:53. [PMID: 37208694 DOI: 10.1186/s13321-023-00716-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space. PROPOSED SOLUTION In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain. RESULTS An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .
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Affiliation(s)
- Katharina Dost
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand.
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany.
| | - Zac Pullar-Strecker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Liam Brydon
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Kunyang Zhang
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Jasmin Hafner
- Eawag-Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600, Dübendorf, Switzerland
| | - Patricia J Riddle
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Jörg S Wicker
- School of Computer Science, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
- enviPath UG & Co. KG, In den Graswiesen 13, 55437, Ockenheim, Germany
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4
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Dekker T, Janssen MAC, Sutherland C, Aben RWM, Scheeren HW, Blanco-Ania D, Rutjes FPJT, Wijtmans M, de Esch IJP. An Automated, Open-Source Workflow for the Generation of (3D) Fragment Libraries. ACS Med Chem Lett 2023; 14:583-590. [PMID: 37197454 PMCID: PMC10184156 DOI: 10.1021/acsmedchemlett.2c00503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/27/2023] [Indexed: 05/19/2023] Open
Abstract
The recent success of fragment-based drug discovery (FBDD) is inextricably linked to adequate library design. To guide the design of our fragment libraries, we have constructed an automated workflow in the open-source KNIME software. The workflow considers chemical diversity and novelty of the fragments, and can also take into account the three-dimensional (3D) character. This design tool can be used to create large and diverse libraries but also to select a small number of representative compounds as a focused set of unique screening compounds to enrich existing fragment libraries. To illustrate the procedures, the design and synthesis of a 10-membered focused library is reported based on the cyclopropane scaffold, which is underrepresented in our existing fragment screening library. Analysis of the focused compound set indicates significant shape diversity and a favorable overall physicochemical profile. By virtue of its modular setup, the workflow can be readily adjusted to design libraries that focus on properties other than 3D shape.
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Affiliation(s)
- Tom Dekker
- Amsterdam
Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Mathilde A. C.
H. Janssen
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Christina Sutherland
- Amsterdam
Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Rene W. M. Aben
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Hans W. Scheeren
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Daniel Blanco-Ania
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Floris P. J. T. Rutjes
- Institute
for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Maikel Wijtmans
- Amsterdam
Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Iwan J. P. de Esch
- Amsterdam
Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
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5
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Cameron AR, Proud AJ, Pearson JK. Machine Learned Composite Methods for Electronic Structure Theory. J Chem Theory Comput 2023; 19:51-60. [PMID: 36507875 DOI: 10.1021/acs.jctc.2c00564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Because of the prohibitive scaling of ab initio techniques for modeling chemical species with high accuracy, they are not generally tractable for large systems. It is therefore of considerable interest to develop high-accuracy computational models with low computational cost that can afford predictions of electronic structure and properties of macromolecular species. Composite methods, as first introduced by Pople [Pople, J. A.; Head-Gordon, M.; Fox, D. J.; Raghavachari, K.; Curtiss, L. A. J. Chem. Phys.1989, 90, 5622.], are an intuitive solution to this problem as they seek to systematically increase accuracy in model chemistries by taking advantage of favorable error cancellation among reasonably low-cost models. By linearly combining a series of carefully chosen model chemistries, the result of a prohibitive-scaling correlated model chemistry with a large basis set may be approximated with relatively good fidelity. However, the full extent to which the choice of low-cost models dictates the predictive accuracy of composite methods is not known, and a full exploration of all model chemistries would be advantageous for the design and validation of a generalizable composite method for widespread application. Here, we show that remarkable accuracy can be generally achieved with composite methods that are more judiciously constructed, leading to increased accuracy with significantly reduced computational cost. By designing a systematic procedure for the automated generation and assessment of over 10 billion unique composite methods, we have extensively explored the space of modern model chemistries to elucidate important design principles in the construction of reliable composite procedures. We anticipate our work to be the starting point in the pursuit of creative approaches to modeling large chemical systems with high accuracy by using novel combinatorial modeling.
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Affiliation(s)
- Andrew R Cameron
- Institute for Quantum Computing, University of Waterloo, Waterloo, OntarioN2L 3G1, Canada.,Department of Physics & Astronomy, University of Waterloo, Waterloo, OntarioN2L 3G1, Canada
| | - Adam J Proud
- Department of Chemistry, University of Prince Edward Island, 550 University Avenue, Charlottetown, Prince Edward IslandC1A 4P3, Canada
| | - Jason K Pearson
- Department of Chemistry, University of Prince Edward Island, 550 University Avenue, Charlottetown, Prince Edward IslandC1A 4P3, Canada
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6
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Rehioui H, Cuissart B, Ouali A, Lepailleur A, Lamotte JL, Bureau R, Zimmermann A. New Pharmacophore Fingerprints and Weight-matrix Learning for Virtual Screening. Application to Bcr-Abl Data. Mol Inform 2023; 42:e2200210. [PMID: 36221998 DOI: 10.1002/minf.202200210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/11/2022] [Indexed: 01/20/2023]
Abstract
In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight-Matrix Learning (WML, based on a feed-forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR-ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K-Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight-Matrix Learning (SWMLR ) leads to clearly superior results in terms of several quality measures.
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Affiliation(s)
- Hajar Rehioui
- GREYC, Normandie Univ., UNICAEN, CNRS - UMR 6072, 14000, Caen, France
| | - Bertrand Cuissart
- GREYC, Normandie Univ., UNICAEN, CNRS - UMR 6072, 14000, Caen, France
| | - Abdelkader Ouali
- GREYC, Normandie Univ., UNICAEN, CNRS - UMR 6072, 14000, Caen, France
| | - Alban Lepailleur
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, Normandie Univ, UNICAEN, CERMN, 14000, Caen, France
| | - Jean-Luc Lamotte
- GREYC, Normandie Univ., UNICAEN, CNRS - UMR 6072, 14000, Caen, France.,Centre d'Etudes et de Recherche sur le Médicament de Normandie, Normandie Univ, UNICAEN, CERMN, 14000, Caen, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, Normandie Univ, UNICAEN, CERMN, 14000, Caen, France
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7
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Bosc N, Felix E, Arcila R, Mendez D, Saunders MR, Green DVS, Ochoada J, Shelat AA, Martin EJ, Iyer P, Engkvist O, Verras A, Duffy J, Burrows J, Gardner JMF, Leach AR. MAIP: a web service for predicting blood-stage malaria inhibitors. J Cheminform 2021; 13:13. [PMID: 33618772 PMCID: PMC7898753 DOI: 10.1186/s13321-021-00487-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 01/20/2021] [Indexed: 12/17/2022] Open
Abstract
Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.
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Affiliation(s)
- Nicolas Bosc
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom.
| | - Eloy Felix
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - Ricardo Arcila
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - David Mendez
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - Martin R Saunders
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Hertfordshire, SG1 2NY, Stevenage, UK
| | - Darren V S Green
- Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Hertfordshire, SG1 2NY, Stevenage, UK
| | - Jason Ochoada
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Tennessee, 38105, Memphis, USA
| | - Anang A Shelat
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Tennessee, 38105, Memphis, USA
| | - Eric J Martin
- Novartis Institute for Biomedical Research, 5300 Chiron Way, California, 94608- 2916, Emeryville, USA
| | - Preeti Iyer
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Andreas Verras
- Schrodinger Inc, 120 West 45th Street, 10036-4041, New York, NY, USA
| | - James Duffy
- Medicines for Malaria Ventures Discovery, 1215, Geneva, Switzerland
| | - Jeremy Burrows
- Medicines for Malaria Ventures Discovery, 1215, Geneva, Switzerland
| | - J Mark F Gardner
- AMG Consultants Ltd, Discovery Park House, Discovery Park, Ramsgate Road, CT13 9ND, Sandwich, Kent, UK
| | - Andrew R Leach
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom.
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8
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Abstract
Virtual screening is no longer merely a matter of identifying the subset of compounds from a large collection likely to be active against a particular endpoint. This viewpoint shares some distinctive practices at Novartis, where virtual screening combines multiple computational tools that marry the competing goals of biasing the selection of compounds toward multiple desired properties, while diversifying the selection to sample the available chemistry space, identifying quality compounds that inform drug discovery. Topics include the various considerations needed for a successful virtual screening practice: triaging, compound quality, accuracy and test sets, activity prediction including multitask modeling, virtual profiling, automation, multiproperty bias, diversity and property spaces, and biased-diversity designs.
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Affiliation(s)
- Eric J Martin
- Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States
| | - Johanna M Jansen
- Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States
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9
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Voicu A, Duteanu N, Voicu M, Vlad D, Dumitrascu V. The rcdk and cluster R packages applied to drug candidate selection. J Cheminform 2020; 12:3. [PMID: 33430987 PMCID: PMC6970292 DOI: 10.1186/s13321-019-0405-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 12/20/2019] [Indexed: 11/10/2022] Open
Abstract
The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a "score" obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward 's method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.
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Affiliation(s)
- Adrian Voicu
- Department of Medical Informatics and Biostatistics, Victor Babes University of Medicine and Pharmacy, E. Murgu 2, 300041, Timisoara, Romania
| | - Narcis Duteanu
- Dep. CAICAM, Politehnica University of Timisoara, Pirvan Boulevard 6, Timisoara, Romania.
| | - Mirela Voicu
- Department of Pharmacology-Clinical Pharmacy, Victor Babes University of Medicine and Pharmacy, E. Murgu 2, 300041, Timisoara, Romania
| | - Daliborca Vlad
- Department of Pharmacology, Victor Babes University of Medicine and Pharmacy, E. Murgu 2, 300041, Timisoara, Romania
| | - Victor Dumitrascu
- Department of Pharmacology, Victor Babes University of Medicine and Pharmacy, E. Murgu 2, 300041, Timisoara, Romania
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10
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Ehrt C, Brinkjost T, Koch O. Binding site characterization - similarity, promiscuity, and druggability. MEDCHEMCOMM 2019; 10:1145-1159. [PMID: 31391887 DOI: 10.1039/c9md00102f] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/31/2019] [Indexed: 12/19/2022]
Abstract
The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany.,Department of Computer Science , TU Dortmund University , Dortmund , Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
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11
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Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France). ENTROPY 2018; 20:e20080570. [PMID: 33265658 PMCID: PMC7513096 DOI: 10.3390/e20080570] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 07/28/2018] [Accepted: 07/30/2018] [Indexed: 11/16/2022]
Abstract
Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.
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12
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Nicholls A. Statistics in molecular modeling: a summary. J Comput Aided Mol Des 2016; 30:279-80. [PMID: 27001050 DOI: 10.1007/s10822-016-9907-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 03/02/2016] [Indexed: 10/22/2022]
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13
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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.
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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
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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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