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Jardim C, de Waal A, Fabris-Rotelli I, Rad NN, Mazarura J, Sherry D. Feature engineered embeddings for classification of molecular data. Comput Biol Chem 2024; 110:108056. [PMID: 38796282 DOI: 10.1016/j.compbiolchem.2024.108056] [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: 11/17/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 05/28/2024]
Abstract
The classification of molecules is of particular importance to the drug discovery process and several other use cases. Data in this domain can be partitioned into structural and sequence/text data. Several techniques such as deep learning are able to classify molecules and predict their functions using both types of data. Molecular structure and encoded chemical information are sufficient to classify a characteristic of a molecule. However, the use of a molecule's structural information typically requires large amounts of computational power with deep learning models that take a long time to train. In this study, we present an alternative approach to molecule classification that addresses the limitations of other techniques. This approach uses natural language processing techniques in the form of count vectorisation, term frequency-inverse document frequency, word2vec and Latent Dirichlet Allocation to feature engineer molecular text data. Through this approach, we aim to make a robust and easily reproducible embedding that is fast to implement and solely dependent on chemical (text) data such as the sequence of a protein. Further, we investigate the usefulness of these embeddings for machine learning models. We apply the techniques to two different types of molecular text data: FASTA sequence data and Simplified Molecular Input Line Entry Specification data. We show that these embeddings provide excellent performance for classification.
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2
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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3
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Tran TTV, Tayara H, Chong KT. Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives. Pharmaceutics 2023; 15:1260. [PMID: 37111744 PMCID: PMC10143484 DOI: 10.3390/pharmaceutics15041260] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University—Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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4
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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5
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Yeh KB, Parekh FK, Mombo I, Leimer J, Hewson R, Olinger G, Fair JM, Sun Y, Hay J. Climate change and infectious disease: A prologue on multidisciplinary cooperation and predictive analytics. Front Public Health 2023; 11:1018293. [PMID: 36741948 PMCID: PMC9895942 DOI: 10.3389/fpubh.2023.1018293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 01/02/2023] [Indexed: 01/22/2023] Open
Abstract
Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. The climate is changing, and the impacts are complex, with multifaceted effects. In addition to connecting climate change and infectious diseases, we aim to draw attention to the challenges of working across multiple disciplines. Doing this requires concentrated efforts in a variety of areas to advance the technological state of the art and at the same time implement ideas and explain to the everyday citizen what is happening. The world's experience with COVID-19 has revealed many gaps in our past approaches to anticipating emerging infectious diseases. Most approaches to predicting outbreaks and identifying emerging microbes of major consequence have been with those causing high morbidity and mortality in humans and animals. These lagging indicators offer limited ability to prevent disease spillover and amplifications in new hosts. Leading indicators and novel approaches are more valuable and now feasible, with multidisciplinary approaches also within our grasp to provide links to disease predictions through holistic monitoring of micro and macro ecological changes. In this commentary, we describe niches for climate change and infectious diseases as well as overarching themes for the important role of collaborative team science, predictive analytics, and biosecurity. With a multidisciplinary cooperative "all call," we can enhance our ability to engage and resolve current and emerging problems.
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Affiliation(s)
| | | | - Illich Mombo
- CIRMF, Franceville, Gabon, Central African Republic
| | | | - Roger Hewson
- UK Health Security Agency, Salisbury, United Kingdom
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Jeanne M. Fair
- Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Yijun Sun
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - John Hay
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
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6
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Osman AM, Arabi AA. Quantum and Classical Evaluations of Carboxylic Acid Bioisosteres: From Capped Moieties to a Drug Molecule. ACS OMEGA 2023; 8:588-598. [PMID: 36643455 PMCID: PMC9835796 DOI: 10.1021/acsomega.2c05708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
Using the Quantum Theory of Atoms in Molecules, the average electron density (AED) tool was developed and employed to quantitatively evaluate the similarities between bioisosteric moieties in drug design. Bioisosteric replacements are valuable in drug molecules to fine-tune their pharmacokinetic and pharmacodynamic properties while maintaining their biological activity. This study was performed on non-classical bioisosteres of carboxylic acid. It was found that the AED of a given bioisostere is generally transferable, within less than 5% difference, irrespective of its environment. It was shown that the AED tool succeeds at depicting not only the similarities of bioisosteric groups but also at highlighting, as counter examples, the differences in non-bioisosteric groups. For the first time, the AED was used to evaluate bioisosterism in an FDA-approved drug molecule, furosemide, and in five analogues of this medicine. In one of the analogues, non-bioisosteric moieties were exchanged, and in four of the analogues, carboxylic acid was replaced with either furan or sulfonamide, and vice versa. It was also found that irrespective of the pH, the AED tool consistently reproduced experimental predictions. The distinct power of the AED tool in quantitatively and precisely measuring the similarity among bioisosteric groups is contrasted with the relatively ambiguous bioisosteric evaluations through the classical qualitative electrostatic potential (ESP) maps. The ESP maps were demonstrated to fail, even qualitatively, in depicting the similarities, in some cases.
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Affiliation(s)
- Alaa M.
A. Osman
- College
of Medicine and Health Sciences, Department of Biochemistry and Molecular
Biology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
| | - Alya A. Arabi
- College
of Medicine and Health Sciences, Department of Biochemistry and Molecular
Biology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
- Centre
for Computational Science, University College
London, 20 Gordon Street, London WC1H 0AJ, U.K.
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Ke W, Crist RM, Clogston JD, Stern ST, Dobrovolskaia MA, Grodzinski P, Jensen MA. Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory. Adv Drug Deliv Rev 2022; 191:114591. [PMID: 36332724 PMCID: PMC9712232 DOI: 10.1016/j.addr.2022.114591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022]
Abstract
Cancer nanotechnologies possess immense potential as therapeutic and diagnostic treatment modalities and have undergone significant and rapid advancement in recent years. With this emergence, the complexities of data standards in the field are on the rise. Data sharing and reanalysis is essential to more fully utilize this complex, interdisciplinary information to answer research questions, promote the technologies, optimize use of funding, and maximize the return on scientific investments. In order to support this, various data-sharing portals and repositories have been developed which not only provide searchable nanomaterial characterization data, but also provide access to standardized protocols for synthesis and characterization of nanomaterials as well as cutting-edge publications. The National Cancer Institute's (NCI) caNanoLab is a dedicated repository for all aspects pertaining to cancer-related nanotechnology data. The searchable database provides a unique opportunity for data mining and the use of artificial intelligence and machine learning, which aims to be an essential arm of future research studies, potentially speeding the design and optimization of next-generation therapies. It also provides an opportunity to track the latest trends and patterns in nanomedicine research. This manuscript provides the first look at such trends extracted from caNanoLab and compares these to similar metrics from the NCI's Nanotechnology Characterization Laboratory, a laboratory providing preclinical characterization of cancer nanotechnologies to researchers around the globe. Together, these analyses provide insight into the emerging interests of the research community and rise of promising nanoparticle technologies.
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Affiliation(s)
- Weina Ke
- Bioinformatics and Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Rachael M Crist
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Jeffrey D Clogston
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Stephan T Stern
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Marina A Dobrovolskaia
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Piotr Grodzinski
- Nanodelivery Systems and Devices Branch, Cancer Imaging Program, National Cancer Institute, Rockville, MD, United States
| | - Mark A Jensen
- Bioinformatics and Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States.
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Arrué L, Cigna-Méndez A, Barbosa T, Borrego-Muñoz P, Struve-Villalobos S, Oviedo V, Martínez-García C, Sepúlveda-Lara A, Millán N, Márquez Montesinos JCE, Muñoz J, Santana PA, Peña-Varas C, Barreto GE, González J, Ramírez D. New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:1914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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Affiliation(s)
- Lily Arrué
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca 3480094, Chile
| | - Alexandra Cigna-Méndez
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Tábata Barbosa
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Paola Borrego-Muñoz
- Escuela de Medicina, Fundación Universitaria Juan N. Corpas, Bogotá 110311, Colombia
| | - Silvia Struve-Villalobos
- Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Temuco 4780000, Chile
| | - Victoria Oviedo
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Claudia Martínez-García
- Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Alexis Sepúlveda-Lara
- Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Temuco 4780000, Chile
| | - Natalia Millán
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | | | - Juana Muñoz
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Paula A. Santana
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Carlos Peña-Varas
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | - George E. Barreto
- Department of Biological Sciences, University of Limerick, V94 T9PX Limerick, Ireland
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - David Ramírez
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
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Welcome to Volume 4 of Future Drug Discovery. FUTURE DRUG DISCOVERY 2022. [DOI: 10.4155/fdd-2022-0006] [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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10
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Lab techniques for a more sustainable world. Biotechniques 2021; 71:501-504. [PMID: 34587814 DOI: 10.2144/btn-2021-0081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Sustainability is an incredibly important topic for people to consider at the moment. Find out how your lab can make changes to reach a more sustainable future in our latest Tech News.
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Abstract
The recent emergence of COVID-19 influenced the layman’s knowledge of drugs. Although several drugs have been discovered serendipitously, research has moved to the next-generation era of drug discovery. The use of drugs is inevitable and they have become lifesavers in the present era. Although research from different scientific backgrounds has supported the translational research of drug discovery, the prime role of pharmacy has to be remembered. Here we have summarized the role of some important subjects in pharmacy education, which have paved different ways in drug discovery and development.
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