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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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Rawat S, Subramaniam K, Subramanian SK, Subbarayan S, Dhanabalan S, Chidambaram SKM, Stalin B, Roy A, Nagaprasad N, Aruna M, Tesfaye JL, Badassa B, Krishnaraj R. Drug Repositioning Using Computer-aided Drug Design (CADD). Curr Pharm Biotechnol 2024; 25:301-312. [PMID: 37605405 DOI: 10.2174/1389201024666230821103601] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/03/2023] [Accepted: 03/20/2023] [Indexed: 08/23/2023]
Abstract
Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.
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Affiliation(s)
- Sona Rawat
- School of Life Sciences, Jaipur National University, Jaipur-302017, India
| | - Kanmani Subramaniam
- Department of Civil Engineering, KPR Institute of Engineering and Technology, Coimbatore-641407, Tamil Nadu, India
| | - Selva Kumar Subramanian
- Department of Sciences, Amrita School of Engineering, Coimbatore - 641112, Tamil Nadu, India
| | - Saravanan Subbarayan
- Department of Civil Engineering, National Institute of Technology, Trichy-620015, Tamil Nadu, India
| | - Subramanian Dhanabalan
- Department of Mechanical Engineering, M. Kumarasamy College of Engineering, Karur - 639113, Tamil Nadu, India
| | | | - Balasubramaniam Stalin
- Department of Mechanical Engineering, Anna University, Regional Campus Madurai, Madurai - 625 019, Tamil Nadu, India
| | - Arpita Roy
- Department of Biotechnology, School of Engineering & Technology, Sharda University, Greater Noida 201310, India
| | - Nagaraj Nagaprasad
- Department of Mechanical Engineering, ULTRA College of Engineering and Technology, Madurai - 625104, Tamilnadu, India
| | - Mahalingam Aruna
- College of Engineering and Computing, Al Ghurair University, Academic City, Dubai, UAE
| | - Jule Leta Tesfaye
- Dambi Dollo University, College of Natural and Computational Science, Department of Physics, Ethiopia
- Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dambi Dollo, Ethiopia
- Ministry of innovation and technology, Ethiopia
| | - Bayissa Badassa
- Department of Mechanical Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia
| | - Ramaswamy Krishnaraj
- Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dambi Dollo, Ethiopia
- Ministry of innovation and technology, Ethiopia
- Department of Mechanical Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia
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3
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Kumar R, Sharma A, Alexiou A, Ashraf GM. Artificial Intelligence in De novo Drug Design: Are We Still There? Curr Top Med Chem 2022; 22:2483-2492. [PMID: 36263480 DOI: 10.2174/1568026623666221017143244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation. OBJECTIVES The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce. METHODS In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field. CONCLUSION The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh, India
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, 2770 NSW, Australia.,AFNP Med Austria, 1010 Wien, Austria
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit (PCRU), King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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4
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Kaitoh K, Yamanishi Y. Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary Chemical Space. J Chem Inf Model 2022; 62:2212-2225. [DOI: 10.1021/acs.jcim.1c01130] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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5
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Abstract
It is still rare that AI application examples with full DMTA (Design, Make, Test, Analysis) outcomes are reported. A recent study highlights that a generative model could be applied in the drug discovery process through an example in which ideas generated by an AI generative model were confirmed by following wet-lab works.
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Affiliation(s)
- Hongming Chen
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health - Guangdong Laboratory), Guangzhou 510530, China
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6
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol Divers 2021; 25:1643-1664. [PMID: 34110579 DOI: 10.1007/s11030-021-10237-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.
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7
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Rashid MBMA. Artificial Intelligence Effecting a Paradigm Shift in Drug Development. SLAS Technol 2020; 26:3-15. [PMID: 32940124 DOI: 10.1177/2472630320956931] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.
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8
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Prykhodko O, Johansson SV, Kotsias PC, Arús-Pous J, Bjerrum EJ, Engkvist O, Chen H. A de novo molecular generation method using latent vector based generative adversarial network. J Cheminform 2019; 11:74. [PMID: 33430938 PMCID: PMC6892210 DOI: 10.1186/s13321-019-0397-9] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 11/23/2019] [Indexed: 02/06/2023] Open
Abstract
Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.
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Affiliation(s)
- Oleksii Prykhodko
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Simon Viet Johansson
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden.
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | | | - Josep Arús-Pous
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Esben Jannik Bjerrum
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden.
- Chemistry and Chemical Biology Centre, Guangzhou Regenerative Medicine and Health-Guangdong Laboratory, Science Park, Guangzhou, China.
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9
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Chen H, Engkvist O. Has Drug Design Augmented by Artificial Intelligence Become a Reality? Trends Pharmacol Sci 2019; 40:806-809. [PMID: 31629547 DOI: 10.1016/j.tips.2019.09.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 09/18/2019] [Indexed: 11/30/2022]
Abstract
The application of artificial intelligence (AI) to drug discovery has become a hot topic in recent years. Generative molecular design based on deep learning is a particular an area of attention. Zhavoronkov et al. recently published a novel approach in which de novo molecular design based on deep learning was used to discover novel potent DDR1 kinase inhibitors. It took 21 days from model building to compound design, and a total of six AI-designed compounds were synthesized and tested. The study highlights how quickly the field of AI-designed compounds is developing, and we can expect further developments in the coming years.
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Affiliation(s)
- Hongming Chen
- Hit Discovery, Discovery Sciences, Biopharmaceutics R&D, AstraZeneca, Gothenburg, Sweden.
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, Biopharmaceutics R&D, AstraZeneca, Gothenburg, Sweden
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10
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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11
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Duarte Y, Márquez-Miranda V, Miossec MJ, González-Nilo F. Integration of target discovery, drug discovery and drug delivery: A review on computational strategies. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2019; 11:e1554. [PMID: 30932351 DOI: 10.1002/wnan.1554] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/14/2018] [Accepted: 01/23/2019] [Indexed: 12/22/2022]
Abstract
Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Yorley Duarte
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Valeria Márquez-Miranda
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Matthieu J Miossec
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Fernando González-Nilo
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile.,Centro Interdisciplinario de Neurociencias de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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12
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Gan J, Wang W, Yang Z, Pan J, Zheng L, Yin L. Prognostic value of pretreatment serum lactate dehydrogenase level in pancreatic cancer patients: A meta-analysis of 18 observational studies. Medicine (Baltimore) 2018; 97:e13151. [PMID: 30431587 PMCID: PMC6257391 DOI: 10.1097/md.0000000000013151] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Several studies were conducted to investigate the prognostic value of pretreatment serum lactate dehydrogenase (LDH) level in pancreatic cancer (PC), but the results were inconsistent. This study aims to comprehensively assess the prognostic value of pretreatment serum LDH level in PC patients by combining the data of the published literatures on this topic. METHODS Embase, PubMed, and Web of Science were completely retrieved until June, 2018. The observational studies focusing on the prognostic value of pretreatment serum LDH level in PC patients were eligible. STATA version 12.0 was used to undertake the statistical analysis. RESULTS Eighteen studies with a total of 3345 patients were included in this meta-analysis. The meta-analysis was conducted to generate pooled hazard ratios (HRs) and 95% confidence interval (CI) for overall survival (OS). Our analysis results suggested that high serum LDH level predicted worse OS (HR 1.57, 95% CI 1.30-1.90, P < .001) in PC patients. Moreover, for patients with advanced PC, the prognostic relevance of pretreatment serum LDH level not only existed in those receiving palliative chemotherapy (HR 1.72, 95% CI 1.35-2.18, P < .001), but also in those who were precluded from chemotherapy (HR 1.91, 95% CI 1.4219-2.58, P < .001). CONCLUSION The meta-analysis results demonstrated that pretreatment serum LDH level is closely associated with OS, and it may be a useful biomarker for assessing the prognosis of PC patients.
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13
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Molecular de-novo design through deep reinforcement learning. J Cheminform 2017; 9:48. [PMID: 29086083 PMCID: PMC5583141 DOI: 10.1186/s13321-017-0235-x] [Citation(s) in RCA: 500] [Impact Index Per Article: 71.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Accepted: 08/23/2017] [Indexed: 01/15/2023] Open
Abstract
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.. ![]()
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14
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Tetko IV, Engkvist O, Koch U, Reymond JL, Chen H. BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry. Mol Inform 2016; 35:615-621. [PMID: 27464907 PMCID: PMC5129546 DOI: 10.1002/minf.201600073] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/06/2016] [Indexed: 01/19/2023]
Abstract
The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.
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Affiliation(s)
- Igor V Tetko
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.,BIGCHEM GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany
| | - Ola Engkvist
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
| | - Uwe Koch
- Lead Discovery Center GmbH, Otto-Hahn Strasse 15, Dortmund, 44227, Germany
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Hongming Chen
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
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15
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Kaneko H, Funatsu K. Applicability Domains and Consistent Structure Generation. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/25/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Hiromasa Kaneko
- Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
| | - Kimito Funatsu
- Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
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16
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Schneider G. De novo design - hop(p)ing against hope. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e453-60. [PMID: 24451634 DOI: 10.1016/j.ddtec.2012.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Current trends in computational de novo design provide a fresh approach to 'scaffold-hopping' in drug discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.
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17
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Spänkuch B, Keppner S, Lange L, Rodrigues T, Zettl H, Koch CP, Reutlinger M, Hartenfeller M, Schneider P, Schneider G. Drugs by numbers: reaction-driven de novo design of potent and selective anticancer leads. Angew Chem Int Ed Engl 2013; 52:4676-81. [PMID: 23166089 DOI: 10.1002/anie.201206897] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2012] [Indexed: 11/07/2022]
Abstract
A potent and selective inhibitor of the anticancer target Polo-like kinase 1 was found by computer-based molecular design. This type II kinase inhibitor was synthesized as suggested by the design software DOGS and exhibited significant antiproliferative effects against HeLa cells without affecting nontransformed cells. The study provides a proof-of-concept for reaction-based de novo design as a leading tool for drug discovery.
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Affiliation(s)
- Birgit Spänkuch
- Universitätsfrauenklinik, Molekulare Onkologie und Gynäkologie, Eberhard Karls Universität, Calwerstrasse 7, 72076 Tübingen, Germany
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Is computer-assisted rescaffolding the future in lead generation? Future Med Chem 2013; 5:237-9. [DOI: 10.4155/fmc.13.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang XP, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FRC, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL. Automated design of ligands to polypharmacological profiles. Nature 2012; 492:215-20. [PMID: 23235874 PMCID: PMC3653568 DOI: 10.1038/nature11691] [Citation(s) in RCA: 599] [Impact Index Per Article: 49.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Accepted: 10/19/2012] [Indexed: 12/22/2022]
Abstract
The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.
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Affiliation(s)
- Jérémy Besnard
- Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK
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Spänkuch B, Keppner S, Lange L, Rodrigues T, Zettl H, Koch CP, Reutlinger M, Hartenfeller M, Schneider P, Schneider G. Wirkstoffe nach Zahlen: reaktionsbasierter De-novo-Entwurf von potenten und selektiven Leitstrukturen für die Krebsforschung. Angew Chem Int Ed Engl 2012. [DOI: 10.1002/ange.201206897] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 2012; 8:e1002380. [PMID: 22359493 PMCID: PMC3280956 DOI: 10.1371/journal.pcbi.1002380] [Citation(s) in RCA: 164] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Accepted: 12/21/2011] [Indexed: 11/19/2022] Open
Abstract
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties. The computer program DOGS aims at the automated generation of new bioactive compounds. Only a single known reference compound is required to have the computer come up with suggestions for potentially isofunctional molecules. A specific feature of the algorithm is its capability to propose a synthesis plan for each designed compound, based on a large set of readily available molecular building blocks and established reaction protocols. The de novo design software provides rapid access to tool compounds and starting points for the development of a lead candidate structure. The manuscript gives a detailed description of the algorithm. Theoretical analysis and prospective case studies demonstrate its ability to propose bioactive, plausible and chemically accessible compounds.
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Schneider G. Designing the molecular future. J Comput Aided Mol Des 2011; 26:115-20. [DOI: 10.1007/s10822-011-9485-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2011] [Accepted: 11/03/2011] [Indexed: 10/15/2022]
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23
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Hartenfeller M, Eberle M, Meier P, Nieto-Oberhuber C, Altmann KH, Schneider G, Jacoby E, Renner S. A collection of robust organic synthesis reactions for in silico molecule design. J Chem Inf Model 2011; 51:3093-8. [PMID: 22077721 DOI: 10.1021/ci200379p] [Citation(s) in RCA: 86] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A focused collection of organic synthesis reactions for computer-based molecule construction is presented. It is inspired by real-world chemistry and has been compiled in close collaboration with medicinal chemists to achieve high practical relevance. Virtual molecules assembled from existing starting material connected by these reactions are supposed to have an enhanced chance to be amenable to real chemical synthesis. About 50% of the reactions in the dataset are ring-forming reactions, which fosters the assembly of novel ring systems and innovative chemotypes. A comparison with a recent survey of the reactions used in early drug discovery revealed considerable overlaps with the collection presented here. The dataset is available encoded as computer-readable Reaction SMARTS expressions from the Supporting Information presented for this paper.
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Affiliation(s)
- Markus Hartenfeller
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland.
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Schneider P, Stutz K, Kasper L, Haller S, Reutlinger M, Reisen F, Geppert T, Schneider G. Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library. Pharmaceuticals (Basel) 2011. [PMCID: PMC4058656 DOI: 10.3390/ph4091236] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.
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Affiliation(s)
| | | | | | | | | | | | | | - Gisbert Schneider
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +41-44-633-7438; Fax: +41-44-633-1379
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Schneider G. From Hits to Leads: Challenges for the Next Phase of Machine Learning in Medicinal Chemistry. Mol Inform 2011; 30:759-63. [DOI: 10.1002/minf.201100070] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Accepted: 05/23/2011] [Indexed: 12/12/2022]
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Hartenfeller M, Schneider G. Enabling future drug discovery by
de novo
design. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.49] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Markus Hartenfeller
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
| | - Gisbert Schneider
- Computer‐Assisted Drug Design, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
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Affiliation(s)
- Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, 8093 Zürich, Switzerland.
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