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Noguchi S, Inoue J. Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery. J Chem Inf Model 2022; 62:5988-6001. [PMID: 36454646 DOI: 10.1021/acs.jcim.2c01345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used recurrent neural network (RNN) assumes monotonically decaying correlations in strings, PixelCNN captures a periodicity among characters of SMILES. Thus, PixelCNN provides us with a novel solution for the analysis of chemical space by extracting the periodicity of molecular structures that will be buried in SMILES. Moreover, this characteristic enables us to generate molecules by combining several simple building blocks, such as a benzene ring and side-chain structures, which contributes to the effective exploration of chemical space by step-by-step searching for molecules from a target fragment. In conclusion, PixelCNN could be a powerful approach focusing on the periodicity of molecules to explore chemical space for the fragment-based molecular design.
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Affiliation(s)
- Satoshi Noguchi
- Department of Advanced Interdisciplinary Studies, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo153-8904, Japan
| | - Junya Inoue
- Institute for Industrial Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba277-0082, Japan.,Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo113-8656, Japan.,Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo153-8904, Japan
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Hermann A, Becker T, Schäfer MA, Hoffmann A, Herres‐Pawlis S. Effective Ligand Design: Zinc Complexes with Guanidine Hydroquinoline Ligands for Fast Lactide Polymerization and Chemical Recycling. CHEMSUSCHEM 2022; 15:e202201075. [PMID: 35803895 PMCID: PMC9795895 DOI: 10.1002/cssc.202201075] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/07/2022] [Indexed: 06/15/2023]
Abstract
In this study, the synthesis of two new guanidine hydroquinoline ligands served as basis for six new zinc guanidine complexes. Two of these complexes showed very high activity in the lactide polymerization under industrial conditions. The lactide polymerization was demonstrated in solution and melt conditions observing high activity and molar masses up to 90 000 g mol-1 . Density functional theory studies elucidated the high activity of the complexes associated with the influence of the ligand backbone and the use of triflate counterions. On the way towards a circular economy, polymerization and depolymerization go hand in hand. So far, guanidine complexes have only shown their good activity in the ring opening polymerization of esters, and guanidine complexes with pure N donors have not been tested in recycling processes. Herein, the excellent ability of zinc guanidine complexes to catalyze both polymerization and depolymerization was demonstrated. The two most promising zinc complexes efficiently mediated the methanolysis of polylactide into methyl lactate under mild reaction conditions.
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Affiliation(s)
- Alina Hermann
- Institute of Inorganic ChemistryRWTH Aachen UniversityLandoltweg 1a52074AachenGermany
| | - Tabea Becker
- Institute of Inorganic ChemistryRWTH Aachen UniversityLandoltweg 1a52074AachenGermany
| | - Martin A. Schäfer
- Institute of Inorganic ChemistryRWTH Aachen UniversityLandoltweg 1a52074AachenGermany
| | - Alexander Hoffmann
- Institute of Inorganic ChemistryRWTH Aachen UniversityLandoltweg 1a52074AachenGermany
| | - Sonja Herres‐Pawlis
- Institute of Inorganic ChemistryRWTH Aachen UniversityLandoltweg 1a52074AachenGermany
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Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Sci Rep 2022; 12:7843. [PMID: 35551258 PMCID: PMC9096754 DOI: 10.1038/s41598-022-11879-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
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Martinelli DD. Generative machine learning for de novo drug discovery: A systematic review. Comput Biol Med 2022; 145:105403. [PMID: 35339849 DOI: 10.1016/j.compbiomed.2022.105403] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 02/08/2023]
Abstract
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecules were encoded either as strings, which were occasionally augmented using randomization, as 2D graphs, or as 3D graphs. Statistical analysis and visualization are performed to illustrate how approaches to machine learning in de novo drug design have evolved over the past five years. Finally, future opportunities and reservations are discussed.
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Grisoni F, Huisman BJH, Button AL, Moret M, Atz K, Merk D, Schneider G. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. SCIENCE ADVANCES 2021; 7:eabg3338. [PMID: 34117066 PMCID: PMC8195470 DOI: 10.1126/sciadv.abg3338] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 04/23/2021] [Indexed: 05/24/2023]
Abstract
Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.
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Affiliation(s)
- Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- Eindhoven University of Technology, Department of Biomedical Engineering, Eindhoven, Netherlands
| | - Berend J H Huisman
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland
| | - Alexander L Button
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland
- University of Lausanne, Department of Computational Biology, Lausanne, Switzerland
| | - Michael Moret
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland
| | - Daniel Merk
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, Frankfurt, Germany
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- ETH Singapore SEC Ltd, Singapore, Singapore
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Schneider P, Welin M, Svensson B, Walse B, Schneider G. Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors. Mol Inform 2020; 39:e2000109. [PMID: 33448694 PMCID: PMC7539333 DOI: 10.1002/minf.202000109] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 06/17/2020] [Indexed: 12/17/2022]
Abstract
Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.
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Affiliation(s)
- Petra Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.,inSili.com GmbH, Segantinisteig 3, 8049, Zurich, Switzerland
| | - Martin Welin
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Bo Svensson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Björn Walse
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
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Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019. [DOI: 78495111110.1038/s41573-019-0050-3' target='_blank'>'"<>78495111110.1038/s41573-019-0050-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1038/s41573-019-0050-3','', '10.1002/open.201900222')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
78495111110.1038/s41573-019-0050-3" />
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8
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Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019; 19:353-364. [PMID: 31801986 DOI: 10.1038/s41573-019-0050-3] [Citation(s) in RCA: 356] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
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