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Skariyachan S, Praveen PKU, Uttarkar A, Niranjan V. Computational design of prospective molecular targets for Burkholderia cepacia complex by molecular docking and dynamic simulation studies. Proteins 2023; 91:724-738. [PMID: 36601892 DOI: 10.1002/prot.26462] [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: 10/14/2022] [Revised: 11/27/2022] [Accepted: 01/02/2023] [Indexed: 01/06/2023]
Abstract
The study aimed to screen prospective molecular targets of BCC and potential natural lead candidates as effective binders by computational modeling, molecular docking, and dynamic (MD) simulation studies. Based on the virulent functions, tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein (mnmC) and pyrimidine/purine nucleoside phosphorylase (ppnP) were selected as the prospective molecular targets. In the absence of experimental data, the three-dimensional (3D) structures of these targets were computationally predicted. After a thorough literature survey and database search, the drug-likeness, and pharmacokinetic properties of 70 natural molecules were computationally predicted and the effectual binding of the best lead molecules against both the targets was predicted by molecular docking. The stabilities of the best-docked complexes were validated by MD simulation and the binding energy calculations were carried out by MM-GBSA approaches. The present study revealed that the hypothetical models of mnmC and ppnP showed stereochemical accuracy. The study also showed that among 70 natural compounds subjected to computational screening, Honokiol (3',5-Di(prop-2-en-1-yl) [1,1'-biphenyl]-2,4'-diol) present in Magnolia showed ideal drug-likeness, pharmacokinetic features and showed effectual binding with mnmC and ppnP (binding energies -7.3 kcal/mol and -6.6 kcal/mol, respectively). The MD simulation and GBSA calculation studies showed that the ligand-protein complexes stabilized throughout tMD simulation. The present study suggests that Honokiol can be used as a potential lead molecule against mnmC and ppnP targets of BCC and this study provides insight into further experimental validation for alternative lead development against drug resistant BCC.
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Affiliation(s)
- Sinosh Skariyachan
- Department of Microbiology, St. Pius X College Rajapuram, Kasaragod, Kerala, India
| | | | - Akshay Uttarkar
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka, India
| | - Vidya Niranjan
- Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka, India
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Yang J, Zhang D, Cai Y, Yu K, Li M, Liu L, Chen X. Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:256-265. [PMID: 35239490 DOI: 10.1109/tcbb.2022.3155453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.
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Kumar R, Sharma A, Alexiou A, Ashraf GM. Artificial Intelligence in De novo Drug Design: Are We Still There? Curr Top Med Chem 2022; 22:2483-2492. [PMID: 36263480 DOI: 10.2174/1568026623666221017143244] [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: 05/03/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation. OBJECTIVES The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce. METHODS In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field. CONCLUSION The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh, India
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, 2770 NSW, Australia.,AFNP Med Austria, 1010 Wien, Austria
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit (PCRU), King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Kulczyk S, Koszytkowska-Stawińska M. Novel drug design framework as a response to neglected and emerging diseases. J Biomol Struct Dyn 2022:1-12. [DOI: 10.1080/07391102.2022.2110519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Stanisław Kulczyk
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
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Chen Y, Rosenkranz C, Hirte S, Kirchmair J. Ring systems in natural products: structural diversity, physicochemical properties, and coverage by synthetic compounds. Nat Prod Rep 2022; 39:1544-1556. [PMID: 35708009 DOI: 10.1039/d2np00001f] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Covering: up to 2021The structural core of most small-molecule drugs is formed by a ring system, often derived from natural products. However, despite the importance of natural product ring systems in bioactive small molecules, there is still a lack of a comprehensive overview and understanding of natural product ring systems and how their full potential can be harnessed in drug discovery and related fields. Herein, we present a comprehensive cheminformatic analysis of the structural and physicochemical properties of 38 662 natural product ring systems, and the coverage of natural product ring systems by readily purchasable, synthetic compounds that are commonly explored in virtual screening and high-throughput screening. The analysis stands out by the use of comprehensive, curated data sets, the careful consideration of stereochemical information, and a robust analysis of the 3D molecular shape and electrostatic properties of ring systems. Among the key findings of this study are the facts that only about 2% of the ring systems observed in NPs are present in approved drugs but that approximately one in two NP ring systems are represented by ring systems with identical or related 3D shape and electrostatic properties in compounds that are typically used in (high-throughput) screening.
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Affiliation(s)
- Ya Chen
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria.
| | - Cara Rosenkranz
- Center for Bioinformatics (ZBH), Universität Hamburg, 20146 Hamburg, Germany
| | - Steffen Hirte
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria. .,Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, 1090 Vienna, Austria
| | - Johannes Kirchmair
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria.
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [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: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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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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8
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Abstract
Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: github.com/ETHmodlab/de_novo_design_RNN .
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Affiliation(s)
- Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- Eindhoven University of Technology, Department of Biomedical Engineering, Eindhoven, Netherlands.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
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9
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Friedrich L, Cingolani G, Ko Y, Iaselli M, Miciaccia M, Perrone MG, Neukirch K, Bobinger V, Merk D, Hofstetter RK, Werz O, Koeberle A, Scilimati A, Schneider G. Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100832. [PMID: 34176236 PMCID: PMC8373093 DOI: 10.1002/advs.202100832] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/16/2021] [Indexed: 05/03/2023]
Abstract
The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.
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Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Gino Cingolani
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Ying‐Hui Ko
- Department of Biochemistry and Molecular BiologySidney Kimmel Cancer CenterThomas Jefferson University1020 Locust StreetPhiladelphiaPA19107USA
| | - Mariaclara Iaselli
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Morena Miciaccia
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Maria Grazia Perrone
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Konstantin Neukirch
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Veronika Bobinger
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
| | - Daniel Merk
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- Institute of Pharmaceutical ChemistryGoethe‐UniversityMax‐von‐Laue Straße 9Frankfurt am Main60438Germany
| | - Robert Klaus Hofstetter
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal ChemistryFriedrich‐Schiller‐University JenaPhilosophenweg 14Jena07743Germany
| | - Andreas Koeberle
- Michael Popp Institute and Center for Molecular Biosciences Innsbruck (CMBI)University of InnsbruckInnsbruck6020Austria
| | - Antonio Scilimati
- Department of Pharmacy – Pharmaceutical SciencesUniversity of BariVia E. Orabona 4Bari70125Italy
| | - Gisbert Schneider
- Department of Chemistry and Applied BiosciencesETH ZurichVladimir‐Prelog‐Weg 4Zurich8093Switzerland
- ETH Singapore SEC Ltd1 CREATE Way, #06‐01 CREATE TowerSingapore138602Singapore
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10
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Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov 2021; 16:949-959. [PMID: 33779453 DOI: 10.1080/17460441.2021.1909567] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Affiliation(s)
- José Jiménez-Luna
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Francesca Grisoni
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an Der Riss, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
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Abstract
Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .
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Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland
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12
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Brown J. Practical Chemogenomic Modeling and Molecule Discovery Strategies Unveiled by Active Learning. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11533-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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13
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Chen Y, Kirchmair J. Cheminformatics in Natural Product-based Drug Discovery. Mol Inform 2020; 39:e2000171. [PMID: 32725781 PMCID: PMC7757247 DOI: 10.1002/minf.202000171] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 07/28/2020] [Indexed: 12/20/2022]
Abstract
This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
- Department of Pharmaceutical ChemistryFaculty of Life SciencesUniversity of Vienna1090ViennaAustria
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14
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Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets 2020; 20:501-521. [PMID: 30360733 DOI: 10.2174/1389450119666181022153016] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/08/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
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15
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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16
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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17
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Friedrich L, Byrne R, Treder A, Singh I, Bauer C, Gudermann T, Mederos Y Schnitzler M, Storch U, Schneider G. Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics. ChemMedChem 2020; 15:566-570. [PMID: 32162837 DOI: 10.1002/cmdc.202000017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/09/2020] [Indexed: 12/22/2022]
Abstract
Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.
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Affiliation(s)
- Lukas Friedrich
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Aaron Treder
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Inderjeet Singh
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany
| | - Christoph Bauer
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Thomas Gudermann
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany.,Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Max-Lebsche-Platz 31, 81377, Munich, Germany
| | - Michael Mederos Y Schnitzler
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Biedersteiner Strasse 29, 80802, Munich, Germany
| | - Ursula Storch
- Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilians University of Munich, Goethestrasse 33, 80336, Munich, Germany.,Institute for Cardiovascular Prevention (IPEK), Ludwig Maximilians University of Munich, Pettenkoferstrasse 8a & 9, 80336, Munich, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
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18
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de Almeida AF, Moreira R, Rodrigues T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 2019. [DOI: 10.1038/s41570-019-0124-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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19
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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20
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Rodrigues T, de Almeida BP, Barbosa-Morais NL, Bernardes GJL. Dissecting celastrol with machine learning to unveil dark pharmacology. Chem Commun (Camb) 2019; 55:6369-6372. [PMID: 31089616 DOI: 10.1039/c9cc03116b] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the antiproliferative activity. We demonstrate how in silico methods may drive systems biology studies for natural products.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal.
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21
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Schneider G, Clark DE. Automated De Novo Drug Design: Are We Nearly There Yet? Angew Chem Int Ed Engl 2019; 58:10792-10803. [PMID: 30730601 DOI: 10.1002/anie.201814681] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Indexed: 11/09/2022]
Abstract
Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.
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Affiliation(s)
- Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David E Clark
- Charles River, 6-9 Spire Green Centre, Harlow, Essex, CM19 5TR, UK
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22
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Schneider G, Clark DE. Automated De Novo Drug Design: Are We Nearly There Yet? Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201814681] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Gisbert Schneider
- ETH ZurichDepartment of Chemistry and Applied Biosciences, RETHINK Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - David E. Clark
- Charles River 6–9 Spire Green Centre Harlow Essex CM19 5TR UK
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23
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Cummings MD, Sekharan S. Structure-Based Macrocycle Design in Small-Molecule Drug Discovery and Simple Metrics To Identify Opportunities for Macrocyclization of Small-Molecule Ligands. J Med Chem 2019; 62:6843-6853. [DOI: 10.1021/acs.jmedchem.8b01985] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Maxwell D. Cummings
- Janssen Research and Development, LLC, Welsh and McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Sivakumar Sekharan
- Cambridge Crystallographic Data Centre, 252 Nassau Street, Princeton, New Jersey 08542, United States
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24
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Sommer K, Flachsenberg F, Rarey M. NAOMInext – Synthetically feasible fragment growing in a structure-based design context. Eur J Med Chem 2019; 163:747-762. [DOI: 10.1016/j.ejmech.2018.11.075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 12/31/2022]
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25
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Chen Y, Stork C, Hirte S, Kirchmair J. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules. Biomolecules 2019; 9:biom9020043. [PMID: 30682850 PMCID: PMC6406893 DOI: 10.3390/biom9020043] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 01/21/2019] [Accepted: 01/21/2019] [Indexed: 01/11/2023] Open
Abstract
Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Conrad Stork
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Steffen Hirte
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany.
- Department of Chemistry, University of Bergen, 5007 Bergen, Norway.
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5008 Bergen, Norway.
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A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:239-271. [PMID: 31621015 DOI: 10.1007/978-3-030-14632-0_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.
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27
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Rodrigues T. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point. Org Biomol Chem 2018; 15:9275-9282. [PMID: 29085945 DOI: 10.1039/c7ob02193c] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal.
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28
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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.
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Abstract
Natural products (NPs) have been used as traditional medicines since antiquity. With more than 1060 estimated compounds with molecular weights less than 500 Da representing chemical space, NPs occupy a very small percentage; however, they are significantly overrepresented in biologically relevant chemical space. The classical approach concentrates on identifying one or more NPs with biological activity from a source organism. There is much more to be learned from NPs than we can discover this narrow view. In this review, we discuss ways to harness the global properties of NPs.
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Affiliation(s)
- Asmaa Boufridi
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
| | - Ronald J Quinn
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
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30
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The potential role of in silico approaches to identify novel bioactive molecules from natural resources. Future Med Chem 2017; 9:1665-1686. [PMID: 28841048 DOI: 10.4155/fmc-2017-0124] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].
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31
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Wu Z, Zhao S, Fash DM, Li Z, Chain WJ, Beutler JA. Englerins: A Comprehensive Review. JOURNAL OF NATURAL PRODUCTS 2017; 80:771-781. [PMID: 28170253 PMCID: PMC6198806 DOI: 10.1021/acs.jnatprod.6b01167] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In the decade since the discovery of englerin A (1) and its potent activity in cancer models, this natural product and its analogues have been the subject of numerous chemical, biological, and preclinical studies by many research groups. This review summarizes published findings and proposes further research directions required for entry of an englerin analogue into clinical trials for kidney cancer and other conditions.
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Affiliation(s)
- Zhenhua Wu
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
| | - Senzhi Zhao
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
| | - David M. Fash
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
| | - Zhenwu Li
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
| | - William J. Chain
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
| | - John A. Beutler
- Molecular Targets Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
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32
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Bebbington MWP. Natural product analogues: towards a blueprint for analogue-focused synthesis. Chem Soc Rev 2017; 46:5059-5109. [DOI: 10.1039/c6cs00842a] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
A review of approaches to natural product analogues leads to the suggestion of new methods for the generation of biologically active natural product-like scaffolds.
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33
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Kumarswamyreddy N, Kesavan V. Stereoselective and Regioselective Assembly of Spirooxindole [2,1-b]furan Motifs through a Tandem Friedel-Crafts Alkylation/5-exo-dig-Cyclization. European J Org Chem 2016. [DOI: 10.1002/ejoc.201601030] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Nandarapu Kumarswamyreddy
- Chemical Biology Laboratory; Department of Biotechnology; Indian Institute of Technology Madras; Bhupat and Jyoti Mehta School of Biosciences; Chennai 600036 India
| | - Venkitasamy Kesavan
- Chemical Biology Laboratory; Department of Biotechnology; Indian Institute of Technology Madras; Bhupat and Jyoti Mehta School of Biosciences; Chennai 600036 India
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34
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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.
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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
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Schneider G, Reker D, Chen T, Hauenstein K, Schneider P, Altmann KH. Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide. Angew Chem Int Ed Engl 2016; 55:12408-11. [PMID: 27605391 DOI: 10.1002/anie.201605707] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 07/11/2016] [Indexed: 11/06/2022]
Abstract
The cyclodepsipeptide doliculide is a marine natural product with strong actin-polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype-selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof-of-concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural-product-inspired chemical biology and drug discovery efforts.
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Affiliation(s)
- Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland.
| | - Daniel Reker
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland
| | - Tao Chen
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland
| | - Kurt Hauenstein
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland
| | - Petra Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland.,inSili.com LLC, Zürich, Switzerland
| | - Karl-Heinz Altmann
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093, Zürich, Switzerland
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36
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Schneider G, Reker D, Chen T, Hauenstein K, Schneider P, Altmann KH. Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201605707] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Gisbert Schneider
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
| | - Daniel Reker
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
| | - Tao Chen
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
| | - Kurt Hauenstein
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
| | - Petra Schneider
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
- inSili.com LLC; Zürich Switzerland
| | - Karl-Heinz Altmann
- Department of Chemistry and Applied Biosciences; Institute of Pharmaceutical Sciences; ETH Zürich; Wolfgang-Pauli-Strasse 10 8093 Zürich Switzerland
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37
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Rodrigues T, Sieglitz F, Somovilla VJ, Cal PMSD, Galione A, Corzana F, Bernardes GJL. Unveiling (-)-Englerin A as a Modulator of L-Type Calcium Channels. Angew Chem Int Ed Engl 2016; 55:11077-81. [PMID: 27391219 PMCID: PMC5042069 DOI: 10.1002/anie.201604336] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Indexed: 11/11/2022]
Abstract
The voltage-dependent L-type Ca(2+) channel was identified as a macromolecular target for (-)-englerin A. This finding was reached by using an unprecedented ligand-based prediction platform and the natural product piperlongumine as a pharmacophore probe. (-)-Englerin A features high substructure dissimilarity to known ligands for voltage-dependent Ca(2+) channels, selective binding affinity for the dihydropyridine site, and potent modulation of calcium signaling in muscle cells and vascular tissue. The observed activity was rationalized at the atomic level by molecular dynamics simulations. Experimental confirmation of this hitherto unknown macromolecular target expands the bioactivity space for this natural product and corroborates the effectiveness of chemocentric computational methods for prioritizing target-based screens and identifying binding counterparts of complex natural products.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal.
| | - Florian Sieglitz
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal
| | - Víctor J Somovilla
- Departamento de Química, Centro de Investigación en Síntesis Química, Universidad de la Rioja, 26006, Logroño, Spain
- Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK
| | - Pedro M S D Cal
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal
| | - Antony Galione
- Department of Pharmacology, University of Oxford, Mansfield Road, OX1 3QT, Oxford, UK
| | - Francisco Corzana
- Departamento de Química, Centro de Investigación en Síntesis Química, Universidad de la Rioja, 26006, Logroño, Spain.
| | - Gonçalo J L Bernardes
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisboa, Portugal. ,
- Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK. ,
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Rodrigues T, Sieglitz F, Somovilla VJ, Cal PMSD, Galione A, Corzana F, Bernardes GJL. Unveiling (−)-Englerin A as a Modulator of L-Type Calcium Channels. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201604336] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular; Faculdade de Medicina da Universidade de Lisboa; Av. Prof. Egas Moniz 1649-028 Lisboa Portugal
| | - Florian Sieglitz
- Instituto de Medicina Molecular; Faculdade de Medicina da Universidade de Lisboa; Av. Prof. Egas Moniz 1649-028 Lisboa Portugal
| | - Víctor J. Somovilla
- Departamento de Química, Centro de Investigación en Síntesis Química; Universidad de la Rioja; 26006 Logroño Spain
- Department of Chemistry; University of Cambridge; Lensfield Road CB2 1EW Cambridge UK
| | - Pedro M. S. D. Cal
- Instituto de Medicina Molecular; Faculdade de Medicina da Universidade de Lisboa; Av. Prof. Egas Moniz 1649-028 Lisboa Portugal
| | - Antony Galione
- Department of Pharmacology; University of Oxford; Mansfield Road OX1 3QT Oxford UK
| | - Francisco Corzana
- Departamento de Química, Centro de Investigación en Síntesis Química; Universidad de la Rioja; 26006 Logroño Spain
| | - Gonçalo J. L. Bernardes
- Instituto de Medicina Molecular; Faculdade de Medicina da Universidade de Lisboa; Av. Prof. Egas Moniz 1649-028 Lisboa Portugal
- Department of Chemistry; University of Cambridge; Lensfield Road CB2 1EW Cambridge UK
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