1
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Krishnan SR, Bung N, Srinivasan R, Roy A. Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process. J Mol Graph Model 2024; 129:108734. [PMID: 38442440 DOI: 10.1016/j.jmgm.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.
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Affiliation(s)
| | - Navneet Bung
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Rajgopal Srinivasan
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, 500081, India.
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2
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Lübbers J, Lessel U, Rarey M. Enhanced Calculation of Property Distributions in Chemical Fragment Spaces. J Chem Inf Model 2024; 64:2008-2020. [PMID: 38466793 DOI: 10.1021/acs.jcim.4c00147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.
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Affiliation(s)
- Justin Lübbers
- ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Hamburg 22761, Germany
| | - Uta Lessel
- Computational Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss 88437, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Hamburg 22761, Germany
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3
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Hönig SMN, Flachsenberg F, Ehrt C, Neumann A, Schmidt R, Lemmen C, Rarey M. SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces. J Comput Aided Mol Des 2024; 38:13. [PMID: 38493240 PMCID: PMC10944417 DOI: 10.1007/s10822-024-00551-7] [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: 12/18/2023] [Accepted: 02/13/2024] [Indexed: 03/18/2024]
Abstract
The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.
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Affiliation(s)
- Sophia M N Hönig
- BioSolveIT, An der Ziegelei 79, 53757, Sankt Augustin, Germany
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | | | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | | | - Robert Schmidt
- BioSolveIT, An der Ziegelei 79, 53757, Sankt Augustin, Germany
| | | | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany.
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4
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Schimunek J, Seidl P, Elez K, Hempel T, Le T, Noé F, Olsson S, Raich L, Winter R, Gokcan H, Gusev F, Gutkin EM, Isayev O, Kurnikova MG, Narangoda CH, Zubatyuk R, Bosko IP, Furs KV, Karpenko AD, Kornoushenko YV, Shuldau M, Yushkevich A, Benabderrahmane MB, Bousquet-Melou P, Bureau R, Charton B, Cirou BC, Gil G, Allen WJ, Sirimulla S, Watowich S, Antonopoulos N, Epitropakis N, Krasoulis A, Itsikalis V, Theodorakis S, Kozlovskii I, Maliutin A, Medvedev A, Popov P, Zaretckii M, Eghbal-Zadeh H, Halmich C, Hochreiter S, Mayr A, Ruch P, Widrich M, Berenger F, Kumar A, Yamanishi Y, Zhang KYJ, Bengio E, Bengio Y, Jain MJ, Korablyov M, Liu CH, Marcou G, Glaab E, Barnsley K, Iyengar SM, Ondrechen MJ, Haupt VJ, Kaiser F, Schroeder M, Pugliese L, Albani S, Athanasiou C, Beccari A, Carloni P, D'Arrigo G, Gianquinto E, Goßen J, Hanke A, Joseph BP, Kokh DB, Kovachka S, Manelfi C, Mukherjee G, Muñiz-Chicharro A, Musiani F, Nunes-Alves A, Paiardi G, Rossetti G, Sadiq SK, Spyrakis F, Talarico C, Tsengenes A, Wade RC, Copeland C, Gaiser J, Olson DR, Roy A, Venkatraman V, Wheeler TJ, Arthanari H, Blaschitz K, Cespugli M, Durmaz V, Fackeldey K, Fischer PD, Gorgulla C, Gruber C, Gruber K, Hetmann M, Kinney JE, Padmanabha Das KM, Pandita S, Singh A, Steinkellner G, Tesseyre G, Wagner G, Wang ZF, Yust RJ, Druzhilovskiy DS, Filimonov DA, Pogodin PV, Poroikov V, Rudik AV, Stolbov LA, Veselovsky AV, De Rosa M, De Simone G, Gulotta MR, Lombino J, Mekni N, Perricone U, Casini A, Embree A, Gordon DB, Lei D, Pratt K, Voigt CA, Chen KY, Jacob Y, Krischuns T, Lafaye P, Zettor A, Rodríguez ML, White KM, Fearon D, Von Delft F, Walsh MA, Horvath D, Brooks CL, Falsafi B, Ford B, García-Sastre A, Yup Lee S, Naffakh N, Varnek A, Klambauer G, Hermans TM. A community effort in SARS-CoV-2 drug discovery. Mol Inform 2024; 43:e202300262. [PMID: 37833243 DOI: 10.1002/minf.202300262] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/15/2023]
Abstract
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
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5
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Zaib S, Rana N, Ali HS, Hussain N, Areeba, Ogaly HA, Al-Zahrani FAM, Khan I. Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2. Int J Biol Macromol 2023; 253:127379. [PMID: 37838109 DOI: 10.1016/j.ijbiomac.2023.127379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan.
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Hafiz Saqib Ali
- INEOS Oxford Institute for Antimicrobial Research and Chemistry Research Laboratory, Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Nadia Hussain
- Department of Pharmaceutical Sciences, College of Pharmacy, Al Ain University, Al Ain, P.O. Box 64141, United Arab Emirates; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, P.O. Box 144534, United Arab Emirates
| | - Areeba
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Hanan A Ogaly
- Chemistry Department, College of Science, King Khalid University, Abha 61421, Saudi Arabia
| | - Fatimah A M Al-Zahrani
- Chemistry Department, College of Science, King Khalid University, Abha 61421, Saudi Arabia
| | - Imtiaz Khan
- Department of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom.
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6
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Zaib S, Rana N, Ali HS, Ur Rehman M, Awwad NS, Ibrahium HA, Khan I. Identification of potential inhibitors targeting yellow fever virus helicase through ligand and structure-based computational studies. J Biomol Struct Dyn 2023:1-18. [PMID: 38109183 DOI: 10.1080/07391102.2023.2294839] [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: 08/14/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Yellow fever is a flavivirus having plus-sensed RNA which encodes a single polyprotein. Host proteases cut this polyprotein into seven nonstructural proteins including a vital NS3 protein. The present study aims to identify the most effective inhibitor against the helicase (NS3) using different advanced ligand and structure-based computational studies. A set of 300 ligands was selected against helicase by chemical structural similarity model, which are similar to S-adenosyl-l-cysteine using infiniSee. This tool screens billions of compounds through a similarity search from in-built chemical spaces (CHEMriya, Galaxi, KnowledgeSpace and REALSpace). The pharmacophore was designed from ligands in the library that showed same features. According to the sequence of ligands, six compounds (29, 87, 99, 116, 148, and 208) were taken for pharmacophore designing against helicase protein. Subsequently, compounds from the library which showed the best pharmacophore shared-features were docked using FlexX functionality of SeeSAR and their optibrium properties were analyzed. Afterward, their ADME was improved by replacing the unfavorable fragments, which resulted in the generation of new compounds. The selected best compounds (301, 302, 303 and 304) were docked using SeeSAR and their pharmacokinetics and toxicological properties were evaluated using SwissADME. The optimal inhibitor for yellow fever helicase was 2-amino-N-(4-(dimethylamino)thiazol-2-yl)-4-methyloxazole-5-carboxamide (302), which exhibits promising potential for drug development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Hafiz Saqib Ali
- Chemistry Research Laboratory, Department of Chemistry and the INEOS Oxford Institute for Antimicrobial Research, University of Oxford, Oxford, UK
| | - Mujeeb Ur Rehman
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Nasser S Awwad
- Department of Chemistry, King Khalid University, Abha, Saudi Arabia
| | - Hala A Ibrahium
- Biology Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Imtiaz Khan
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
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Dera AA, Zaib S, Hussain N, Rana N, Javed H, Khan I. Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer. Molecules 2023; 28:4850. [PMID: 37375404 DOI: 10.3390/molecules28124850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a target protein along with EGFR because of its limited tyrosine kinase activity and ability to activate PI3/AKT pathway responsible for therapy failure. We herein used a BioSolveIT suite to identify potent inhibitors of EGFR and HER3. The schematic process involves screening of databases for constructing compound library comprising of 903 synthetic compounds (602 for EGFR and 301 for HER3) followed by pharmacophore modeling. The best docked poses of compounds with the druggable binding site of respective proteins were selected according to pharmacophore designed by SeeSAR version 12.1.0. Subsequently, preclinical analysis was performed via an online server SwissADME and potent inhibitors were selected. Compound 4k and 4m were the most potent inhibitors of EGFR while 7x effectively inhibited the binding site of HER3. The binding energies of 4k, 4m, and 7x were -7.7, -6.3 and -5.7 kcal/mol, respectively. Collectively, 4k, 4m and 7x showed favorable interactions with the most druggable binding sites of their respective proteins. Finally, in silico pre-clinical testing by SwissADME validated the non-toxic nature of compounds 4k, 4m and 7x providing a promising treatment option for chemoresistant NSCLC.
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Affiliation(s)
- Ayed A Dera
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 62529, Saudi Arabia
| | - Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Nadia Hussain
- Department of Pharmaceutical Sciences, College of Pharmacy, Al Ain University, Al Ain P.O. Box 64141, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi P.O. Box 144534, United Arab Emirates
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Hira Javed
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Imtiaz Khan
- Department of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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Jung S, Vatheuer H, Czodrowski P. VSFlow: an open-source ligand-based virtual screening tool. J Cheminform 2023; 15:40. [PMID: 37004101 PMCID: PMC10064649 DOI: 10.1186/s13321-023-00703-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 02/18/2023] [Indexed: 04/03/2023] Open
Abstract
Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.
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Affiliation(s)
- Sascha Jung
- grid.5675.10000 0001 0416 9637Department of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Helge Vatheuer
- grid.5675.10000 0001 0416 9637Department of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Paul Czodrowski
- grid.5802.f0000 0001 1941 7111Department of Chemistry, Johannes Gutenberg University Mainz, Duesbergweg 10-14, 55128 Mainz, Germany
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9
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Atz K, Guba W, Grether U, Schneider G. Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods Mol Biol 2023; 2576:477-493. [PMID: 36152211 DOI: 10.1007/978-1-0716-2728-0_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
- ETH Singapore SEC Ltd, Singapore, Singapore
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10
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Bellmann L, Klein R, Rarey M. Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces. J Chem Inf Model 2022; 62:2800-2810. [PMID: 35653228 DOI: 10.1021/acs.jcim.2c00334] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The distributions of physicochemical property values, like the octanol-water partition coefficient, are routinely calculated to describe and compare virtual chemical libraries. Traditionally, these distributions are derived by processing each member of a library individually and summarizing all values in a distribution. This process becomes impractical when operating on chemical spaces which surpass billions of compounds in size. In this work, we present a novel algorithmic method called SpaceProp for the property distribution calculation of large nonenumerable combinatorial fragment spaces. The novel method follows a combinatorial approach and is able to calculate physicochemical property distributions of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and OTAVA's CHEMriya Space for the first time. Furthermore, we present a first approach of optimizing property distributions directly in combinatorial fragment spaces.
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Affiliation(s)
- Louis Bellmann
- Universität Hamburg, ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Bundesstraße 43, 20146 Hamburg, Germany
| | - Raphael Klein
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Bundesstraße 43, 20146 Hamburg, Germany
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11
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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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12
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Warr WA, Nicklaus MC, Nicolaou CA, Rarey M. Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 2022; 62:2021-2034. [PMID: 35421301 DOI: 10.1021/acs.jcim.2c00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
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Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Crewe, Cheshire CW4 7HZ, United Kingdom
| | - Marc C Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Christos A Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Matthias Rarey
- Universität Hamburg, ZBH Center for Bioinformatics, 20146 Hamburg, Germany
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13
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Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
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Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
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14
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Bellmann L, Penner P, Gastreich M, Rarey M. Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs. J Chem Inf Model 2022; 62:553-566. [PMID: 35050621 DOI: 10.1021/acs.jcim.1c01378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The set of chemical compounds shared by two or more chemical libraries is assessed routinely as means of comparing these libraries for various applications. Traditionally this is achieved by comparing the members of the chemical libraries individually for identity. This approach becomes impractical when operating on chemical libraries exceeding billions or even trillions of compounds in size. As a result, no such analysis exists for ultralarge chemical spaces like the Enamine REAL Space containing over 20 billion compounds. In this work, we present a novel tool called SpaceCompare for the overlap calculation of large, nonenumerable combinatorial fragment spaces. In contrast to existing methods, SpaceCompare utilizes topological fingerprints and the combinatorial character of these chemical spaces. The tool is able to determine the exact overlap of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and Otava's CHEMriya for the first time.
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Affiliation(s)
- Louis Bellmann
- Universität Hamburg, ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Bundesstraße 43, 20146 Hamburg, Germany
| | - Patrick Penner
- Universität Hamburg, ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Bundesstraße 43, 20146 Hamburg, Germany
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Research Group for Computational Molecular Design, Bundesstraße 43, 20146 Hamburg, Germany
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15
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Gavrylenko K, Horvath D, Klimchuk O, Oksiuta O, Marcou G, Varnek A. SynthI: A New Open-Source Tool for Synthon-Based Library Design. J Chem Inf Model 2021; 62:2151-2163. [PMID: 34723532 DOI: 10.1021/acs.jcim.1c00754] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github.com/Laboratoire-de-Chemoinformatique/SynthI.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd.78 Chervonotkatska str., 02660 Kyiv, Ukraine
| | - Sergey V Ryabukhin
- The Institute of High Technologies, Kyiv National Taras Shevchenko University, 64 Volodymyrska Street, Kyiv 01601, Ukraine.,Enamine Ltd.78 Chervonotkatska str., 02660 Kyiv, Ukraine
| | - Kostiantyn Gavrylenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kyiv, Ukraine.,Enamine Ltd.78 Chervonotkatska str., 02660 Kyiv, Ukraine
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Olga Klimchuk
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Oleksandr Oksiuta
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
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16
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Rarey M, Nicklaus MC, Warr W. Call for Papers for the Special Issue: From Reaction Informatics to Chemical Space. J Chem Inf Model 2021. [DOI: 10.1021/acs.jcim.1c00321] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Matthias Rarey
- Universität Hamburg, ZBH−Center for Bioinformatics, 20146 Hamburg, Germany
| | - Marc C. Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Wendy Warr
- Wendy Warr & Associates, Cheshire CW4 7HZ, U.K
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17
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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: 83] [Impact Index Per Article: 27.7] [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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18
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Grygorenko OO, Radchenko DS, Dziuba I, Chuprina A, Gubina KE, Moroz YS. Generating Multibillion Chemical Space of Readily Accessible Screening Compounds. iScience 2020; 23:101681. [PMID: 33145486 PMCID: PMC7593547 DOI: 10.1016/j.isci.2020.101681] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/17/2020] [Accepted: 10/10/2020] [Indexed: 11/25/2022] Open
Abstract
An approach to the generation of ultra-large chemical libraries of readily accessible (“REAL”) compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation – deprotection – acylation of monoprotected diamines or amide formation – click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and “beyond rule-of-five” members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million. A strategy for ultra-large readily accessible (REAL) compound libraries is described Pre-validated two- or three-step three-component reaction sequences are used A 29-billion chemical space with ∼80% synthesis success rate has been easily obtained
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19
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Saldívar-González FI, Huerta-García CS, Medina-Franco JL. Chemoinformatics-based enumeration of chemical libraries: a tutorial. J Cheminform 2020; 12:64. [PMID: 33372622 PMCID: PMC7590480 DOI: 10.1186/s13321-020-00466-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/05/2020] [Indexed: 11/10/2022] Open
Abstract
Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.
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Affiliation(s)
- Fernanda I. Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
| | - C. Sebastian Huerta-García
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
| | - José L. Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
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20
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Bellmann L, Penner P, Rarey M. Topological Similarity Search in Large Combinatorial Fragment Spaces. J Chem Inf Model 2020; 61:238-251. [PMID: 33084338 DOI: 10.1021/acs.jcim.0c00850] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In similarity-driven virtual screening, molecular fingerprints are widely used to assess the similarity of all compounds contained in a chemical library to a query compound of interest. This similarity analysis is traditionally done for each member of the library individually. When encoding chemical spaces that surpass billions of compounds in size, it becomes impractical to enumerate all their products, let alone assess their similarity, deeming this approach impossible without investing a substantial amount of resources. In this work, we present a novel search algorithm named SpaceLight for topological fingerprint similarity searching in large, practically non-enumerable combinatorial fragment spaces. In contrast to existing methods, SpaceLight is able to utilize the combinatorial character of these chemical spaces for efficiency while maintaining a high correlation of the description of molecular similarity to well-known molecular fingerprints like ECFP. The resulting software is able to search prominent spaces like EnamineREAL with more than 10 billion compounds in seconds on a standard desktop computer.
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Affiliation(s)
- Louis Bellmann
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
| | - Patrick Penner
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
| | - Matthias Rarey
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
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21
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Grygorenko OO, Volochnyuk DM, Ryabukhin SV, Judd DB. The Symbiotic Relationship Between Drug Discovery and Organic Chemistry. Chemistry 2019; 26:1196-1237. [PMID: 31429510 DOI: 10.1002/chem.201903232] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/19/2019] [Indexed: 12/20/2022]
Abstract
All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.
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Affiliation(s)
- Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Dmitriy M Volochnyuk
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine.,Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kiev, 02660, Ukraine
| | - Sergey V Ryabukhin
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Duncan B Judd
- Awridian Ltd., Stevenage Bioscience Catalyst, Gunnelswood Road, Stevenage, Herts, SG1 2FX, UK
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22
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Lessel U, Lemmen C. Comparison of Large Chemical Spaces. ACS Med Chem Lett 2019; 10:1504-1510. [PMID: 31620241 DOI: 10.1021/acsmedchemlett.9b00331] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/11/2019] [Indexed: 12/20/2022] Open
Abstract
Chemical libraries are commonplace in computer-aided drug discovery, and assessing their overlap/complementarity is a routine task. For this purpose, different techniques are applied, ranging from exact matching to comparing physicochemical properties. However, these techniques are applicable only if the compound sets are not too big. Particularly for chemical spaces, containing billions of compounds, alternative ways of assessment are required. Random subsets could be enumerated and compared one-to-one, but given the vast sizes of the chemical spaces assessed here, such samples can at best provide a rough estimate of any overlap. Here we describe a novel way to compare chemical spaces utilizing a panel of query compounds. We applied this technique to three different types of spaces and obtained insight into their structural overlap, their coverage of the chemical universe, and their density. As chemical feasibility of virtual compounds is particularly important, we included related in silico predictions in our assessment.
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Affiliation(s)
- Uta Lessel
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Christian Lemmen
- BioSolveIT GmbH, An der Ziegelei 79, 53757 St. Augustin, Germany
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23
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Yoshimori A, Horita Y, Tanoue T, Bajorath J. Method for Systematic Analogue Search Using the Mega SAR Matrix Database. J Chem Inf Model 2019; 59:3727-3734. [PMID: 31468964 DOI: 10.1021/acs.jcim.9b00557] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Analogue searching is a typical requirement in hit expansion, hit-to-lead, and lead optimization projects. A new computational methodology is introduced to search for existing and virtual analogues of active compounds. The approach is based upon the SAR matrix (SARM) data structure that was originally developed for the systematic identification and structural organization of analogue series. The SARM-based analogue search algorithm further extends the capacity of current substructure-based methods by (i) simultaneously considering existing and virtual analogues that populate chemical space around query compounds, (ii) permitting not only R-group replacements but also well-defined chemical modifications in core structures to further expand the analogue space, and (iii) automatically extracting all possible analogues from large pools. In addition, as a basis for analogue searching following the SARM concept, the Mega-SARM database is introduced. Mega-SARM is derived from nearly 3.7 million compounds and contains ∼250 000 matrices with structurally related analogue series and more than 1.5 million virtual candidate compounds.
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Affiliation(s)
- Atsushi Yoshimori
- Institute for Theoretical Medicine, Inc. , 26-1 Muraoka-Higashi 2-chome , Fujisawa , Kanagawa 251-0012 , Japan
| | - Yuichi Horita
- INFOGRAM, Inc. , 2-17-19 Yasuda Building No. 5 3F, Hakataekimae, Hakata-ku , Fukuoka City , Fukuoka 812-0011 , Japan
| | - Toru Tanoue
- INFOGRAM, Inc. , 2-17-19 Yasuda Building No. 5 3F, Hakataekimae, Hakata-ku , Fukuoka City , Fukuoka 812-0011 , Japan
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Endenicher Allee 19c , D-53115 Bonn , Germany
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24
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The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today 2019; 24:1148-1156. [PMID: 30851414 DOI: 10.1016/j.drudis.2019.02.013] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/01/2019] [Accepted: 02/28/2019] [Indexed: 10/27/2022]
Abstract
Recent innovations have brought pharmacophore-driven methods for navigating virtual chemical spaces, the size of which can reach into the billions of molecules, to the fingertips of every chemist. There has been a paradigm shift in the underlying computational chemistry that drives chemical space search applications, incorporating intelligent reaction knowledge into their core so that they can readily deliver commercially available molecules as nearest neighbor hits from within giant virtual spaces. These vast resources enable medicinal chemists to execute rapid scaffold-hopping experiments, rapid hit expansion, and structure-activity relationship (SAR) exploitation in largely intellectual property (IP)-free territory and at unparalleled low cost.
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25
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van Hilten N, Chevillard F, Kolb P. Virtual Compound Libraries in Computer-Assisted Drug Discovery. J Chem Inf Model 2019; 59:644-651. [PMID: 30624918 DOI: 10.1021/acs.jcim.8b00737] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.
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Affiliation(s)
- Niek van Hilten
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
| | - Florent Chevillard
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
| | - Peter Kolb
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
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26
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Cheminformatics in the Service of GPCR Drug Discovery. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2018; 1705:395-411. [PMID: 29188575 DOI: 10.1007/978-1-4939-7465-8_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Cheminformatics is a broad discipline covering a wide range of computational approaches, including the characterization of molecular similarity, pattern recognition, and predictive modeling. The unifying theme that these apparently disparate methods have in common is the aim of extracting useable information from the increasing amounts of data that are associated with contemporary drug discovery projects. Both proprietary and publically available data can be exploited to help inform and improve the process of developing novel therapeutic molecules targeting the GPCR family of proteins.
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27
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Valeur E, Jimonet P. New Modalities, Technologies, and Partnerships in Probe and Lead Generation: Enabling a Mode-of-Action Centric Paradigm. J Med Chem 2018; 61:9004-9029. [DOI: 10.1021/acs.jmedchem.8b00378] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Eric Valeur
- Medicinal Chemistry, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal 431 83, Sweden
| | - Patrick Jimonet
- External Innovation Drug Discovery, Global Business Development & Licensing, Sanofi, 13 quai Jules Guesde, 94400 Vitry-sur-Seine, France
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Nagamani S, Gaur AS, Tanneeru K, Muneeswaran G, Madugula SS, Consortium M, Druzhilovskiy D, Poroikov VV, Sastry GN. Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:913-926. [PMID: 29206500 DOI: 10.1080/1062936x.2017.1402819] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 11/06/2017] [Indexed: 06/07/2023]
Abstract
Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.
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Affiliation(s)
- S Nagamani
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | - A S Gaur
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | - K Tanneeru
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | - G Muneeswaran
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | - S S Madugula
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
| | | | | | - V V Poroikov
- b Institute of Biomedical Chemistry , Moscow , Russia
| | - G N Sastry
- a Centre for Molecular Modeling , CSIR-Indian Institute of Chemical Technology , Hyderabad , India
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29
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Does ‘Big Data’ exist in medicinal chemistry, and if so, how can it be harnessed? Future Med Chem 2016; 8:1801-1806. [DOI: 10.4155/fmc-2016-0163] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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30
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Muegge I, Bergner A, Kriegl JM. Computer-aided drug design at Boehringer Ingelheim. J Comput Aided Mol Des 2016; 31:275-285. [DOI: 10.1007/s10822-016-9975-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 09/15/2016] [Indexed: 12/18/2022]
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31
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Nicolaou CA, Watson IA, Hu H, Wang J. The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space. J Chem Inf Model 2016; 56:1253-66. [DOI: 10.1021/acs.jcim.6b00173] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Christos A. Nicolaou
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Ian A. Watson
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Hong Hu
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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32
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Ring system-based chemical graph generation for de novo molecular design. J Comput Aided Mol Des 2016; 30:425-46. [PMID: 27299746 DOI: 10.1007/s10822-016-9916-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/31/2016] [Indexed: 10/21/2022]
Abstract
Generating chemical graphs in silico by combining building blocks is important and fundamental in virtual combinatorial chemistry. A premise in this area is that generated structures should be irredundant as well as exhaustive. In this study, we develop structure generation algorithms regarding combining ring systems as well as atom fragments. The proposed algorithms consist of three parts. First, chemical structures are generated through a canonical construction path. During structure generation, ring systems can be treated as reduced graphs having fewer vertices than those in the original ones. Second, diversified structures are generated by a simple rule-based generation algorithm. Third, the number of structures to be generated can be estimated with adequate accuracy without actual exhaustive generation. The proposed algorithms were implemented in structure generator Molgilla. As a practical application, Molgilla generated chemical structures mimicking rosiglitazone in terms of a two dimensional pharmacophore pattern. The strength of the algorithms lies in simplicity and flexibility. Therefore, they may be applied to various computer programs regarding structure generation by combining building blocks.
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33
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Wasko MJ, Pellegrene KA, Madura JD, Surratt CK. A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets. Front Neurol 2015; 6:197. [PMID: 26441817 PMCID: PMC4566055 DOI: 10.3389/fneur.2015.00197] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 08/24/2015] [Indexed: 01/12/2023] Open
Abstract
Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.
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Affiliation(s)
- Michael J Wasko
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Kendy A Pellegrene
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Jeffry D Madura
- Department of Chemistry and Biochemistry, Center for Computational Sciences, Bayer School of Natural and Environmental Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Christopher K Surratt
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
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34
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Chevillard F, Kolb P. SCUBIDOO: A Large yet Screenable and Easily Searchable Database of Computationally Created Chemical Compounds Optimized toward High Likelihood of Synthetic Tractability. J Chem Inf Model 2015; 55:1824-35. [PMID: 26282054 DOI: 10.1021/acs.jcim.5b00203] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
De novo drug design is widely assisted by computational approaches that enable the generation of a tremendous amount of new virtual molecules within a short time frame. While the novelty of the computationally generated compounds can easily be assessed, such approaches often neglect the synthetic feasibility of the molecules, thus creating a potential hurdle that can be a barrier to further investigation. Therefore, we have developed SCUBIDOO, a freely accessible database concept that currently holds 21 million virtual products originating from a small library of building blocks and a collection of robust organic reactions. This large data set was reduced to three representative and computationally tractable samples denoted as S, M, and L, containing 9994, 99,977, and 999,794 products, respectively. These small sets are useful as starting points for ligand identification and optimization projects. The generated products come with synthesis instructions and alerts of possible side reactions, and we show that they exhibit drug-like properties while still extending into unexplored quadrants of chemical space, thus suggesting novelty. We show multiple examples that demonstrate how SCUBIDOO can facilitate the search around initial hits. This database might be a useful idea generator for early ligand discovery projects since it allows a focus on those molecules that are likely to be synthetically feasible and can therefore be studied further. Together with its modular building block construction principle, this database is also suitable for structure-activity relationship studies or fragment-growing strategies.
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Affiliation(s)
- F Chevillard
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , 35032 Marburg, Germany
| | - P Kolb
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , 35032 Marburg, Germany
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35
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Schultes S, Kooistra AJ, Vischer HF, Nijmeijer S, Haaksma EEJ, Leurs R, de Esch IJP, de Graaf C. Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors. J Chem Inf Model 2015; 55:1030-44. [PMID: 25815783 DOI: 10.1021/ci500694c] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.
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Affiliation(s)
- Sabine Schultes
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Albert J Kooistra
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Henry F Vischer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Saskia Nijmeijer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Eric E J Haaksma
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Rob Leurs
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Iwan J P de Esch
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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36
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3D virtual screening of large combinatorial spaces. Methods 2015; 71:14-20. [DOI: 10.1016/j.ymeth.2014.06.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 06/18/2014] [Accepted: 06/19/2014] [Indexed: 12/16/2022] Open
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37
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Beck B, Geppert T. Industrial applications of in silico ADMET. J Mol Model 2014; 20:2322. [PMID: 24972798 DOI: 10.1007/s00894-014-2322-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 05/27/2014] [Indexed: 11/26/2022]
Abstract
Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.
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Affiliation(s)
- Bernd Beck
- Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstrasse 65, 88397, Biberach an der Riss, Germany,
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38
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Peng Z. Very large virtual compound spaces: construction, storage and utility in drug discovery. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 10:e387-e394. [PMID: 24050135 DOI: 10.1016/j.ddtec.2013.01.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.
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39
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Ehrlich HC, Henzler AM, Rarey M. Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. J Chem Inf Model 2013; 53:1676-88. [PMID: 23751070 DOI: 10.1021/ci400107k] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Retrieving molecules with specific structural features is a fundamental requirement of today's molecular database technologies. Estimates claim the chemical space relevant for drug discovery to be around 10⁶⁰ molecules. This figure is many orders of magnitude larger than the amount of molecules conventional databases retain today and will store in the future. An elegant description of such a large chemical space is provided by the concept of fragment spaces. A fragment space comprises fragments that are molecules with open valences and describes rules how to connect these fragments to products. Due to the combinatorial nature of fragment spaces, a complete enumeration of its products is intractable. We present an algorithm to search fragment spaces for generic chemical patterns as present in the SMARTS chemical pattern language. Our method allows specification of the chemical surrounding of an atom in a query and, therefore, enables a chemically intuitive search. During the search, the costly enumeration of products is avoided. The result is a fragment space that exactly describes all possible molecules that contain the user-defined pattern. We evaluated the algorithm in three different drug development use-cases and performed a large scale statistical analysis with 738 SMARTS patterns on three public available fragment spaces. Our results show the ability of the algorithm to explore the chemical space around known active molecules, to analyze fragment spaces for the presence of likely toxic molecules, and to identify complex macromolecular structures under additional structural constraints. By searching the fragment space in its nonenumerated form, spaces covering up to 10¹⁹ molecules can be examined in times ranging between 47 s and 19 min depending on the complexity of the query pattern.
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40
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Wellenzohn B, Lessel U, Beller A, Isambert T, Hoenke C, Nosse B. Identification of New Potent GPR119 Agonists by Combining Virtual Screening and Combinatorial Chemistry. J Med Chem 2012; 55:11031-41. [DOI: 10.1021/jm301549a] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bernd Wellenzohn
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Uta Lessel
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Andreas Beller
- Research Germany/Medicinal Chemistry/Combinatorial Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Timo Isambert
- Research Germany/Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Christoph Hoenke
- Research Germany/Medicinal Chemistry/Combinatorial Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Bernd Nosse
- Research Germany/Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
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41
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Ehrlich HC, Volkamer A, Rarey M. Searching for Substructures in Fragment Spaces. J Chem Inf Model 2012. [DOI: 10.1021/ci300283a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Andrea Volkamer
- University of Hamburg, Bundestraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- University of Hamburg, Bundestraße 43, 20146 Hamburg, Germany
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42
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Hu Q, Peng Z, Sutton SC, Na J, Kostrowicki J, Yang B, Thacher T, Kong X, Mattaparti S, Zhou JZ, Gonzalez J, Ramirez-Weinhouse M, Kuki A. Pfizer Global Virtual Library (PGVL): a chemistry design tool powered by experimentally validated parallel synthesis information. ACS COMBINATORIAL SCIENCE 2012; 14:579-89. [PMID: 23020747 DOI: 10.1021/co300096q] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An unprecedented amount of parallel synthesis information was accumulated within Pfizer over the past 12 years. This information was captured by an informatics tool known as PGVL (Pfizer Global Virtual Library). PGVL was used for many aspects of drug discovery including automated reactant mining and reaction product formation to build a synthetically feasible virtual compound collection. In this report, PGVL is discussed in detail. The chemistry information within PGVL has been used to extract synthesis and design information using an intuitive desktop Graphic User Interface, PGVL Hub. Several real-case examples of PGVL are also presented.
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Affiliation(s)
- Qiyue Hu
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Zhengwei Peng
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Scott C. Sutton
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Jim Na
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Jaroslav Kostrowicki
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Bo Yang
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Thomas Thacher
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Xianjun Kong
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Sarathy Mattaparti
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Joe Zhongxiang Zhou
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Javier Gonzalez
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Michele Ramirez-Weinhouse
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
| | - Atsuo Kuki
- Pfizer Global Research and Development, La Jolla Laboratories, 10770 Science Center Drive, San Diego, California
92121, United States
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43
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IADE: a system for intelligent automatic design of bioisosteric analogs. J Comput Aided Mol Des 2012; 26:1207-15. [DOI: 10.1007/s10822-012-9609-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2012] [Accepted: 09/20/2012] [Indexed: 11/26/2022]
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44
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Vainio MJ, Kogej T, Raubacher F. Automated recycling of chemistry for virtual screening and library design. J Chem Inf Model 2012; 52:1777-86. [PMID: 22657574 DOI: 10.1021/ci300157m] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An early stage drug discovery project needs to identify a number of chemically diverse and attractive compounds. These hit compounds are typically found through high-throughput screening campaigns. The diversity of the chemical libraries used in screening is therefore important. In this study, we describe a virtual high-throughput screening system called Virtual Library. The system automatically "recycles" validated synthetic protocols and available starting materials to generate a large number of virtual compound libraries, and allows for fast searches in the generated libraries using a 2D fingerprint based screening method. Virtual Library links the returned virtual hit compounds back to experimental protocols to quickly assess the synthetic accessibility of the hits. The system can be used as an idea generator for library design to enrich the screening collection and to explore the structure-activity landscape around a specific active compound.
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Affiliation(s)
- Mikko J Vainio
- Discovery Sciences Computational Sciences, AstraZeneca R&D, Pepparedsleden 1, S-43183 Mölndal, Sweden
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45
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Christ CD, Zentgraf M, Kriegl JM. Mining electronic laboratory notebooks: analysis, retrosynthesis, and reaction based enumeration. J Chem Inf Model 2012; 52:1745-56. [PMID: 22657734 DOI: 10.1021/ci300116p] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An approach to automatically analyze and use the knowledge contained in electronic laboratory notebooks (ELNs) has been developed. Reactions were reduced to their reactive center and converted to a string representation (SMIRKS) which formed the basis for reaction classification and in silico (retro-)synthesis. Of the SMIRKS that occurred at least five times, 98% successfully regenerated the original product. The extracted reaction rules (SMIRKS) and corresponding reactants span a virtual chemical space which showed a strong dependence on the size of the reactive center. Whereas relatively few robust reaction types were sufficient to describe a large part of all reactions, considerably more reaction rules were necessary to cover all reactions in the ELN. Furthermore, reaction sequences were extracted to identify frequent combinations and diversifying reaction steps. Based on the extracted knowledge a (retro-)synthesis tool was built allowing for de novo design of compounds which have a high chance of being synthetically accessible. In an example application of the de novo design tool, various feasible retrosynthetic routes to the query molecule were obtained. Reaction based enumeration along the top ranked route yielded a library of 29 920 compounds with diverse properties, 99.9% of which are novel in the sense that they are unknown to the public domain.
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Affiliation(s)
- Clara D Christ
- Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstrasse 65, 88397 Biberach an der Riss, Germany.
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46
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Schuffenhauer A. Computational methods for scaffold hopping. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1106] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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47
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Sheng C, Zhang W. Fragment Informatics and Computational Fragment-Based Drug Design: An Overview and Update. Med Res Rev 2012; 33:554-98. [DOI: 10.1002/med.21255] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Chunquan Sheng
- Department of Medicinal Chemistry; School of Pharmacy; Second Military Medical University; 325 Guohe Road Shanghai 200433 People's Republic of China
| | - Wannian Zhang
- Department of Medicinal Chemistry; School of Pharmacy; Second Military Medical University; 325 Guohe Road Shanghai 200433 People's Republic of China
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48
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Lessel U, Wellenzohn B, Fischer JR, Rarey M. Design of Combinatorial Libraries for the Exploration of Virtual Hits from Fragment Space Searches with LoFT. J Chem Inf Model 2011; 52:373-9. [DOI: 10.1021/ci2003957] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Uta Lessel
- Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an der Riss, Germany
| | - Bernd Wellenzohn
- Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an der Riss, Germany
| | - J. Robert Fischer
- Center
for Bioinformatics Hamburg, University of Hamburg, Bundesstr. 43, D-20146 Hamburg
| | - Matthias Rarey
- Center
for Bioinformatics Hamburg, University of Hamburg, Bundesstr. 43, D-20146 Hamburg
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Fischer JR, Lessel U, Rarey M. Improving Similarity-Driven Library Design: Customized Matching and Regioselective Feature Trees. J Chem Inf Model 2011; 51:2156-63. [DOI: 10.1021/ci200014g] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- J. Robert Fischer
- Center for Bioinformatics (ZBH), University of Hamburg, Hamburg, Germany
| | - Uta Lessel
- Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Matthias Rarey
- Center for Bioinformatics (ZBH), University of Hamburg, Hamburg, Germany
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Boehm M. Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries. ACTA ACUST UNITED AC 2011. [DOI: 10.1002/9783527633326.ch1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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