1
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Katz LS, Visser EJ, Plitzko KF, Pennings M, Cossar PJ, Tse IL, Kaiser M, Brunsveld L, Scott DK, Ottmann C. Molecular glues of the regulatory ChREBP/14-3-3 complex protect beta cells from glucolipotoxicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.16.580675. [PMID: 38405965 PMCID: PMC10888794 DOI: 10.1101/2024.02.16.580675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The Carbohydrate Response Element Binding Protein (ChREBP) is a glucose-responsive transcription factor (TF) that is characterized by two major splice isoforms (α and β). In acute hyperglycemia, both ChREBP isoforms regulate adaptive β-expansion; however, during chronic hyperglycemia and glucolipotoxicity, ChREBPβ expression surges, leading to β-cell dedifferentiation and death. 14-3-3 binding to ChREBPα results in its cytoplasmic retention and concomitant suppression of transcriptional activity, suggesting that small molecule-mediated stabilization of this protein-protein interaction (PPI) via molecular glues may represent an attractive entry for the treatment of metabolic disease. Here, we show that structure-based optimizations of a molecular glue tool compound led not only to more potent ChREBPα/14-3-3 PPI stabilizers but also for the first time cellular active compounds. In primary human β-cells, the most active compound stabilized the ChREBPα/14-3-3 interaction and thus induced cytoplasmic retention of ChREBPα, resulting in highly efficient β-cell protection from glucolipotoxicity while maintaining β-cell identity. This study may thus not only provide the basis for the development of a unique class of compounds for the treatment of Type 2 Diabetes but also showcases an alternative 'molecular glue' approach for achieving small molecule control of notoriously difficult targetable TFs.
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Affiliation(s)
- Liora S Katz
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Emira J Visser
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Kathrin F Plitzko
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
| | - Marloes Pennings
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Peter J Cossar
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Isabelle L Tse
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Markus Kaiser
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
| | - Luc Brunsveld
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
| | - Donald K Scott
- Diabetes, Obesity and Metabolism Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1152, New York, 10029, USA
| | - Christian Ottmann
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
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2
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Brocklehurst CE, Altmann E, Bon C, Davis H, Dunstan D, Ertl P, Ginsburg-Moraff C, Grob J, Gosling DJ, Lapointe G, Marziale AN, Mues H, Palmieri M, Racine S, Robinson RI, Springer C, Tan K, Ulmer W, Wyler R. MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery. J Med Chem 2024; 67:2118-2128. [PMID: 38270627 DOI: 10.1021/acs.jmedchem.3c02029] [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: 01/26/2024]
Abstract
We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.
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Affiliation(s)
- Cara E Brocklehurst
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Eva Altmann
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Corentin Bon
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Holly Davis
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - David Dunstan
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Peter Ertl
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Carol Ginsburg-Moraff
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Jonathan Grob
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Daniel J Gosling
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Guillaume Lapointe
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Alexander N Marziale
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Heinrich Mues
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Marco Palmieri
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Sophie Racine
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
| | - Richard I Robinson
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Clayton Springer
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - Kian Tan
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - William Ulmer
- Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
| | - René Wyler
- Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
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3
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Ha T, Lee D, Kwon Y, Park MS, Lee S, Jang J, Choi B, Jeon H, Kim J, Choi H, Seo HT, Choi W, Hong W, Park YJ, Jang J, Cho J, Kim B, Kwon H, Kim G, Oh WS, Kim JW, Choi J, Min M, Jeon A, Jung Y, Kim E, Lee H, Choi YS. AI-driven robotic chemist for autonomous synthesis of organic molecules. SCIENCE ADVANCES 2023; 9:eadj0461. [PMID: 37910607 PMCID: PMC10619927 DOI: 10.1126/sciadv.adj0461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023]
Abstract
The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors.
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Affiliation(s)
- Taesin Ha
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Dongseon Lee
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Min Sik Park
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Sangyoon Lee
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Jaejun Jang
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Byungkwon Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Hyunjeong Jeon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Jeonghun Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Hyundo Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Hyung-Tae Seo
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
- Department of Mechanical Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16227, Republic of Korea
| | - Wonje Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Wooram Hong
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Young Jin Park
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
- School of Mechanical Engineering, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do, Republic of Korea
| | - Junwon Jang
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Joonkee Cho
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Bosung Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Hyukju Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Gahee Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Won Seok Oh
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Jin Woo Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Joonhyuk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Minsik Min
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Aram Jeon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Yongsik Jung
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
| | - Eunji Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
- School of Business Administration, Chung-Ang University, 135, Seodal-ro, Dongjak-gu, Seoul 06973, Republic of Korea
| | - Hyosug Lee
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
- College of Information and Communication Engineering, Sungkyunkwan University (SKKU), 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Republic of Korea
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4
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Buskes M, Coffin A, Troast DM, Stein R, Blanco MJ. Accelerating Drug Discovery: Synthesis of Complex Chemotypes via Multicomponent Reactions. ACS Med Chem Lett 2023; 14:376-385. [PMID: 37077380 PMCID: PMC10107905 DOI: 10.1021/acsmedchemlett.3c00012] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/20/2023] [Indexed: 04/21/2023] Open
Abstract
The generation of multiple bonds in one reaction step has attracted massive interest in drug discovery and development. Multicomponent reactions (MCRs) offer the advantage of combining three or more reagents in a one-pot fashion to effectively yield a synthetic product. This approach significantly accelerates the synthesis of relevant compounds for biological testing. However, there is a perception that this methodology will only produce simple chemical scaffolds with limited use in medicinal chemistry. In this Microperspective, we want to highlight the value of MCRs toward the synthesis of complex molecules characterized by the presence of quaternary and chiral centers. This paper will cover specific examples showing the impact of this technology toward the discovery of clinical compounds and recent breakthroughs to expand the scope of the reactions toward topologically rich molecular chemotypes.
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Affiliation(s)
- Melissa
J. Buskes
- Atavistik Bio 75 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Aaron Coffin
- Atavistik Bio 75 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Dawn M. Troast
- Atavistik Bio 75 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Rachel Stein
- Atavistik Bio 75 Sidney Street, Cambridge, Massachusetts 02139, United States
| | - Maria-Jesus Blanco
- Atavistik Bio 75 Sidney Street, Cambridge, Massachusetts 02139, United States
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5
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Lim YY, Zaidi AMA, Miskon A. Combining Copper and Zinc into a Biosensor for Anti-Chemoresistance and Achieving Osteosarcoma Therapeutic Efficacy. Molecules 2023; 28:2920. [PMID: 37049685 PMCID: PMC10096333 DOI: 10.3390/molecules28072920] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 04/14/2023] Open
Abstract
Due to its built-up chemoresistance after prolonged usage, the demand for replacing platinum in metal-based drugs (MBD) is rising. The first MBD approved by the FDA for cancer therapy was cisplatin in 1978. Even after nearly four and a half decades of trials, there has been no significant improvement in osteosarcoma (OS) therapy. In fact, many MBD have been developed, but the chemoresistance problem raised by platinum remains unresolved. This motivates us to elucidate the possibilities of the copper and zinc (CuZn) combination to replace platinum in MBD. Thus, the anti-chemoresistance properties of CuZn and their physiological functions for OS therapy are highlighted. Herein, we summarise their chelators, main organic solvents, and ligand functions in their structures that are involved in anti-chemoresistance properties. Through this review, it is rational to discuss their ligands' roles as biosensors in drug delivery systems. Hereafter, an in-depth understanding of their redox and photoactive function relationships is provided. The disadvantage is that the other functions of biosensors cannot be elaborated on here. As a result, this review is being developed, which is expected to intensify OS drugs with higher cure rates. Nonetheless, this advancement intends to solve the major chemoresistance obstacle towards clinical efficacy.
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Affiliation(s)
- Yan Yik Lim
- Faculty of Defence Science and Technology, National Defence University of Malaysia, Sungai Besi Camp, Kuala Lumpur 57000, Malaysia
| | - Ahmad Mujahid Ahmad Zaidi
- Faculty of Defence Science and Technology, National Defence University of Malaysia, Sungai Besi Camp, Kuala Lumpur 57000, Malaysia
| | - Azizi Miskon
- Faculty of Engineering, National Defence University of Malaysia, Sungai Besi Camp, Kuala Lumpur 57000, Malaysia
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6
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Peng X, Wang X. Next-generation intelligent laboratories for materials design and manufacturing. MRS BULLETIN 2023; 48:179-185. [PMID: 36960275 PMCID: PMC9970134 DOI: 10.1557/s43577-023-00481-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
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Affiliation(s)
- Xiting Peng
- Department of Chemical Engineering, Tsinghua University, Beijing, China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing, China
- Key Laboratory of Industrial Biocatalysis (Tsinghua University), Ministry of Education, Beijing, China
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7
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McMillan AE, Wu WWX, Nichols PL, Wanner BM, Bode JW. A vending machine for drug-like molecules - automated synthesis of virtual screening hits. Chem Sci 2022; 13:14292-14299. [PMID: 36545137 PMCID: PMC9749103 DOI: 10.1039/d2sc05182f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022] Open
Abstract
As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 1012 members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.
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Affiliation(s)
- Angus E. McMillan
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Wilson W. X. Wu
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Paula L. Nichols
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland,Synple Chem AGKemptpark 18Kemptthal 8310Switzerland
| | | | - Jeffrey W. Bode
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
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8
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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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9
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Zhang H, Gao C, Zhang L, Yu R, Kang C. Homology modeling, virtual screening and MD simulations for identification of NUAK1 and ULK1 potential dual inhibitors. NEW J CHEM 2022. [DOI: 10.1039/d1nj03690d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Cancer cells produce more reactive oxygen species (ROS) due to their severe metabolic stress. SNF1 like kinase 1 (NUAK1) is the key part of the cellular antioxidant system. Inhibiting the...
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10
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Liao Q, Chen Z, Tao Y, Zhang B, Wu X, Yang L, Wang Q, Wang Z. An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro. Sci Rep 2021; 11:22796. [PMID: 34815498 PMCID: PMC8611036 DOI: 10.1038/s41598-021-02266-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/12/2021] [Indexed: 02/06/2023] Open
Abstract
The current severe situation of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been reversed and posed great threats to global health. Therefore, there is an urgent need to find out effective antiviral drugs. The 3-chymotrypsin-like protease (3CLpro) in SARS-CoV-2 serve as a promising anti-virus target due to its essential role in the regulation of virus reproduction. Here, we report an improved integrated approach to identify effective 3CLpro inhibitors from effective Chinese herbal formulas. With this approach, we identified the 5 natural products (NPs) including narcissoside, kaempferol-3-O-gentiobioside, rutin, vicenin-2 and isoschaftoside as potential anti-SARS-CoV-2 candidates. Subsequent molecular dynamics simulation additionally revealed that these molecules can be tightly bound to 3CLpro and confirmed effectiveness against COVID-19. Moreover, kaempferol-3-o-gentiobioside, vicenin-2 and isoschaftoside were first reported to have SARS-CoV-2 3CLpro inhibitory activity. In summary, this optimized integrated strategy for drug screening can be utilized in the discovery of antiviral drugs to achieve rapid acquisition of drugs with specific effects on antiviral targets.
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Affiliation(s)
- Qi Liao
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyu Chen
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanlin Tao
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Beibei Zhang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaojun Wu
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Li Yang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Qingzhong Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Zhengtao Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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11
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Joshi RP, Kumar N. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Molecules 2021; 26:6761. [PMID: 34833853 PMCID: PMC8619999 DOI: 10.3390/molecules26226761] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/29/2021] [Indexed: 11/23/2022] Open
Abstract
Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.
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Affiliation(s)
| | - Neeraj Kumar
- Computational Biology Group, Biological Science Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USA;
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12
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Conformational plasticity of the ULK3 kinase domain. Biochem J 2021; 478:2811-2823. [PMID: 34190988 DOI: 10.1042/bcj20210257] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 01/11/2023]
Abstract
The human protein kinase ULK3 regulates the timing of membrane abscission, thus being involved in exosome budding and cytokinesis. Herein, we present the first high-resolution structures of the ULK3 kinase domain. Its unique features are explored against the background of other ULK kinases. An inhibitor fingerprint indicates that ULK3 is highly druggable and capable of adopting a wide range of conformations. In accordance with this, we describe a conformational switch between the active and an inactive ULK3 conformation, controlled by the properties of the attached small-molecule binder. Finally, we discuss a potential substrate-recognition mechanism of the full-length ULK3 protein.
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13
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Nichols PL. Automated and enabling technologies for medicinal chemistry. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:191-272. [PMID: 34147203 DOI: 10.1016/bs.pmch.2021.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists.
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Affiliation(s)
- Paula L Nichols
- Synple Chem AG, Kemptthal, Switzerland; ETH, Zurich, Switzerland.
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14
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Vaucher AC, Schwaller P, Geluykens J, Nair VH, Iuliano A, Laino T. Inferring experimental procedures from text-based representations of chemical reactions. Nat Commun 2021; 12:2573. [PMID: 33958589 PMCID: PMC8102565 DOI: 10.1038/s41467-021-22951-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 04/07/2021] [Indexed: 11/19/2022] Open
Abstract
The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.
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Affiliation(s)
| | | | | | | | - Anna Iuliano
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Pisa, Italy
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15
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Gao K, Shaabani S, Xu R, Zarganes-Tzitzikas T, Gao L, Ahmadianmoghaddam M, Groves MR, Dömling A. Nanoscale, automated, high throughput synthesis and screening for the accelerated discovery of protein modifiers. RSC Med Chem 2021; 12:809-818. [PMID: 34124680 PMCID: PMC8152715 DOI: 10.1039/d1md00087j] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 04/14/2021] [Indexed: 11/26/2022] Open
Abstract
Hit finding in early drug discovery is often based on high throughput screening (HTS) of existing and historical compound libraries, which can limit chemical diversity, is time-consuming, very costly, and environmentally not sustainable. On-the-fly compound synthesis and in situ screening in a highly miniaturized and automated format has the potential to greatly reduce the medicinal chemistry environmental footprint. Here, we used acoustic dispensing technology to synthesize a library in a 1536 well format based on the Groebcke-Blackburn-Bienaymé reaction (GBB-3CR) on a nanomole scale. The unpurified library was screened by differential scanning fluorimetry (DSF) and cross-validated using microscale thermophoresis (MST) against the oncogenic protein-protein interaction menin-MLL. Several GBB reaction products were found as μM menin binder, and the structural basis of the interactions with menin was elucidated by co-crystal structure analysis. Miniaturization and automation of the organic synthesis and screening process can lead to an acceleration in the early drug discovery process, which is an alternative to classical HTS and a step towards the paradigm of continuous manufacturing.
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Affiliation(s)
- Kai Gao
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Shabnam Shaabani
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Ruixue Xu
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Tryfon Zarganes-Tzitzikas
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Li Gao
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Maryam Ahmadianmoghaddam
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Matthew R Groves
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
| | - Alexander Dömling
- Pharmacy Department, Drug Design group, University of Groningen A. Deusinglaan 1 9700 AD Groningen The Netherlands
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16
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PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. PLoS One 2021; 16:e0241728. [PMID: 33901196 PMCID: PMC8075191 DOI: 10.1371/journal.pone.0241728] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 04/01/2021] [Indexed: 12/23/2022] Open
Abstract
The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 97.7% in the Receiver Operating Characteristic curve (ROC-AUC) and 65.5% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained three (3) potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.
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17
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Daley SK, Cordell GA. Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Nat Prod Commun 2021. [DOI: 10.1177/1934578x211003029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.
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Affiliation(s)
| | - Geoffrey A. Cordell
- Natural Products Inc., Evanston, IL, USA
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
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18
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909989] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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19
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Kumar M, Papaleo E. A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy. Sci Rep 2020; 10:14874. [PMID: 32913252 PMCID: PMC7483646 DOI: 10.1038/s41598-020-71527-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.
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Affiliation(s)
- Mukesh Kumar
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.
- Translational Disease System Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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20
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Struble T, Alvarez JC, Brown SP, Chytil M, Cisar J, DesJarlais RL, Engkvist O, Frank SA, Greve DR, Griffin DJ, Hou X, Johannes JW, Kreatsoulas C, Lahue B, Mathea M, Mogk G, Nicolaou CA, Palmer AD, Price DJ, Robinson RI, Salentin S, Xing L, Jaakkola T, Green WH, Barzilay R, Coley CW, Jensen KF. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J Med Chem 2020; 63:8667-8682. [PMID: 32243158 PMCID: PMC7457232 DOI: 10.1021/acs.jmedchem.9b02120] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Indexed: 12/20/2022]
Abstract
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.
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Affiliation(s)
- Thomas
J. Struble
- Department
of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Juan C. Alvarez
- Computational
and Structural Chemistry, Merck & Co.
Inc., Kenilworth, New Jersey 07033, United States
| | - Scott P. Brown
- Sunovion
Pharmaceuticals Inc., Marlborough, Massachusetts 01752, United States
| | - Milan Chytil
- Sunovion
Pharmaceuticals Inc., Marlborough, Massachusetts 01752, United States
| | - Justin Cisar
- Janssen
Research & Development LLC, Spring House, Pennsylvania 19477, United States
| | - Renee L. DesJarlais
- Janssen
Research & Development LLC, Spring House, Pennsylvania 19477, United States
| | - Ola Engkvist
- Hit
Discovery, Discovery Sciences, R&D, AstraZeneca, 431 83 Mölndal, Sweden
| | - Scott A. Frank
- Eli Lilly
and Company, Indianapolis, Indiana 46285, United States
| | - Daniel R. Greve
- LEO
Pharma A/S, Industriparken 55, DK-2750 Ballerup, Denmark
| | | | - Xinjun Hou
- Pfizer
Inc., Cambridge, Massachusetts 02139, United States
| | - Jeffrey W. Johannes
- Medicinal Chemistry, Early Oncology, Oncology
R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | | | - Brian Lahue
- Computational
and Structural Chemistry, Merck & Co.
Inc., Kenilworth, New Jersey 07033, United States
| | - Miriam Mathea
- BASF
SE, Carl-Bosch-Strasse
38, 67056 Ludwigshafen
am Rhein, Germany
| | | | | | - Andrew D. Palmer
- BASF
SE, Carl-Bosch-Strasse
38, 67056 Ludwigshafen
am Rhein, Germany
| | - Daniel J. Price
- GlaxoSmithKline, Collegeville, Pennsylvania 19426, United States
| | - Richard I. Robinson
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States
| | | | - Li Xing
- WuXi
AppTec, Cambridge, Massachusetts 02142, United States
| | - Tommi Jaakkola
- Computer
Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, United States
| | - William. H. Green
- Department
of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Regina Barzilay
- Computer
Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, United States
| | - Connor W. Coley
- Department
of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Klavs F. Jensen
- Department
of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
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21
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Farrant E. Automation of Synthesis in Medicinal Chemistry: Progress and Challenges. ACS Med Chem Lett 2020; 11:1506-1513. [PMID: 32832016 PMCID: PMC7430952 DOI: 10.1021/acsmedchemlett.0c00292] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/16/2020] [Indexed: 12/13/2022] Open
Abstract
Since the 1990s, concerted attempts have been made to improve the efficiency of medicinal chemistry synthesis tasks using automation. Although impacts have been seen in some tasks, such as small array synthesis and reaction optimization, many synthesis tasks in medicinal chemistry are still manual. As it has been shown that synthesis technology has a large effect on the properties of the compounds being tested, this review looks at recent research in automation relevant to synthesis in medicinal chemistry. A common theme has been the integration of tasks, as well as the use of increased computing power to access complex automation platforms remotely and to improve synthesis planning software. However, there has been more limited progress in modular tools for the medicinal chemist with a focus on autonomy rather than automation.
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Affiliation(s)
- Elizabeth Farrant
- New Path Molecular Research
Ltd, Building 580, Babraham
Research Campus, Cambridge CB22 3AT, U.K.
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22
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Sijbesma E, Visser E, Plitzko K, Thiel P, Milroy LG, Kaiser M, Brunsveld L, Ottmann C. Structure-based evolution of a promiscuous inhibitor to a selective stabilizer of protein-protein interactions. Nat Commun 2020; 11:3954. [PMID: 32770072 PMCID: PMC7414219 DOI: 10.1038/s41467-020-17741-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/10/2020] [Indexed: 01/01/2023] Open
Abstract
The systematic stabilization of protein–protein interactions (PPI) has great potential as innovative drug discovery strategy to target novel and hard-to-drug protein classes. The current lack of chemical starting points and focused screening opportunities limits the identification of small molecule stabilizers that engage two proteins simultaneously. Starting from our previously described virtual screening strategy to identify inhibitors of 14-3-3 proteins, we report a conceptual molecular docking approach providing concrete entries for discovery and rational optimization of stabilizers for the interaction of 14-3-3 with the carbohydrate-response element-binding protein (ChREBP). X-ray crystallography reveals a distinct difference in the binding modes between weak and general inhibitors of 14-3-3 complexes and a specific, potent stabilizer of the 14-3-3/ChREBP complex. Structure-guided stabilizer optimization results in selective, up to 26-fold enhancement of the 14-3-3/ChREBP interaction. This study demonstrates the potential of rational design approaches for the development of selective PPI stabilizers starting from weak, promiscuous PPI inhibitors. Small molecule stabilizers of protein–protein interactions hold great therapeutic potential. Based on virtual screening and molecular docking, the authors here develop a strategy to evolve weak, promiscuous inhibitors of 14-3-3 interactions into selective stabilizers of the 14-3-3/ChREBP complex.
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Affiliation(s)
- Eline Sijbesma
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Emira Visser
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Kathrin Plitzko
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany
| | - Philipp Thiel
- Institute for Biomedical Informatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Lech-Gustav Milroy
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Markus Kaiser
- Chemical Biology, Center of Medical Biotechnology, Faculty of Biology, University of Duisburg-Essen, Duisburg, Germany.
| | - Luc Brunsveld
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
| | - Christian Ottmann
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. .,Department of Organic Chemistry, University of Duisburg-Essen, Duisburg, Germany.
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23
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angew Chem Int Ed Engl 2020; 59:23414-23436. [PMID: 31553509 DOI: 10.1002/anie.201909989] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/19/2023]
Abstract
This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.
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Affiliation(s)
- Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Natalie S Eyke
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Klavs F Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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24
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Nicolaou CA, Watson IA, LeMasters M, Masquelin T, Wang J. Context Aware Data-Driven Retrosynthetic Analysis. J Chem Inf Model 2020; 60:2728-2738. [DOI: 10.1021/acs.jcim.9b01141] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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
| | - Mark LeMasters
- Research Chemistry IT, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Thierry Masquelin
- 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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25
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Godfrey AG, Michael SG, Sittampalam GS, Zahoránszky-Köhalmi G. A Perspective on Innovating the Chemistry Lab Bench. Front Robot AI 2020; 7:24. [PMID: 33501193 PMCID: PMC7805875 DOI: 10.3389/frobt.2020.00024] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 02/12/2020] [Indexed: 12/16/2022] Open
Abstract
Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.
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Affiliation(s)
- Alexander G. Godfrey
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
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26
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Duncan KK, Rudnicki DD, Austin CP, Tagle DA. Exploring Novel Biologically-Relevant Chemical Space Through Artificial Intelligence: The NCATS ASPIRE Program. Front Robot AI 2020; 6:143. [PMID: 33501158 PMCID: PMC7805902 DOI: 10.3389/frobt.2019.00143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 12/09/2019] [Indexed: 11/13/2022] Open
Abstract
In recent years, artificial intelligence (AI)/machine learning (ML; a subset of AI) have become increasingly important to the biomedical research community. These technologies, coupled to big data and cheminformatics, have tremendous potential to improve the design of novel therapeutics and to provide safe and effective drugs to patients. A National Center for Advancing Translational Sciences (NCATS) program called A Specialized Platform for Innovative Research Exploration (ASPIRE) leverages advances in AI/ML, automated synthetic chemistry, and high-throughput biology, and seeks to enable translation and drug development by catalyzing exploration of biologically active chemical space. Here we discuss the opportunities and challenges surrounding the application of AI/ML to the exploration of novel biologically relevant chemical space as part of ASPIRE.
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Affiliation(s)
| | | | | | - Danilo A. Tagle
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
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Vidler LR, Baumgartner MP. Creating a Virtual Assistant for Medicinal Chemistry. ACS Med Chem Lett 2019; 10:1051-1055. [PMID: 31312407 DOI: 10.1021/acsmedchemlett.9b00151] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/06/2019] [Indexed: 11/28/2022] Open
Abstract
The virtual assistant concept is one that many technology companies have taken on despite having other well-developed and popular user interfaces. We wondered whether it would be possible to create an effective virtual assistant for a medicinal chemistry organization, the key being delivering the information the user would want to see, directly to them, at the right time. We introduce Kernel, an early prototype virtual assistant created at Lilly, and a number of examples of the scenarios that have been implemented to try to demonstrate the concept. A biochemical assay summary email is described that brings together new results and some basic analysis, delivered within an hour of new data appearing for that assay, and an email delivering new compound design ideas directly to the original submitter of a compound shortly after their compound was tested for the first time. We conclude with a high level description of the first example of a Design-Make-Test-Analyze cycle completed in the absence of any human intellectual input at Lilly. We believe that this concept has much potential in changing the way that computational results and analysis are delivered and consumed within a medicinal chemistry group, and we hope to inspire others to implement their own similar solutions.
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Affiliation(s)
- Lewis R. Vidler
- Research and Development, Eli Lilly and Company Ltd., Sunninghill Road, Windlesham, Surrey GU20 6PH, United Kingdom
| | - Matthew P. Baumgartner
- Lilly Biotechnology Center, Eli Lilly and Company, San Diego, California 92121, United States
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Parry DM. Closing the Loop: Developing an Integrated Design, Make, and Test Platform for Discovery. ACS Med Chem Lett 2019; 10:848-856. [PMID: 31223437 DOI: 10.1021/acsmedchemlett.9b00095] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/06/2019] [Indexed: 11/30/2022] Open
Abstract
The relatively slow cycle time within medicinal chemistry from synthesis to assay is constantly being challenged to help improve the efficiency of the discovery process. While both synthesis and assay have been automated to varying degrees, there has, until recently, been limited focus on the complete design, make, and test process. This Innovations article outlines the development of Cyclofluidic from inception through to the commercialization of a fully integrated closed loop design, synthesis, and screen platform.
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Affiliation(s)
- David M. Parry
- Cyclofluidic Ltd., BioPark, Broadwater Road, Welwyn Garden City AL7 3AX, U.K
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