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Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Inf Sci Syst 2024; 12:41. [PMID: 39130617 PMCID: PMC11310389 DOI: 10.1007/s13755-024-00300-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 07/27/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.
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Affiliation(s)
- Hongyu Chen
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Lu
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyi Xiao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Shensuo Li
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luan
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guangyong Zheng
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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2
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Du J, Yang C, Deng Y, Guo H, Gu M, Chen D, Liu X, Huang J, Yan W, Liu J. Discovery of AMPs from random peptides via deep learning-based model and biological activity validation. Eur J Med Chem 2024; 277:116797. [PMID: 39197254 DOI: 10.1016/j.ejmech.2024.116797] [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: 06/24/2024] [Revised: 07/31/2024] [Accepted: 08/22/2024] [Indexed: 09/01/2024]
Abstract
The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of Staphylococcus aureus wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.
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Affiliation(s)
- Jun Du
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China; Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China
| | - Changyan Yang
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China; Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China
| | - Yabo Deng
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China
| | - Hai Guo
- The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
| | - Mengyun Gu
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China
| | - Danna Chen
- Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China
| | - Xia Liu
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China.
| | - Jinqi Huang
- Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
| | - Wenjin Yan
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China.
| | - Jian Liu
- Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China.
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3
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Li X, Liu S, Liu D, Yu M, Wu X, Wang H. Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity. Clin Pharmacokinet 2024; 63:1239-1249. [PMID: 39225885 DOI: 10.1007/s40262-024-01416-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2024] [Indexed: 09/04/2024]
Abstract
In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.
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Affiliation(s)
- Xiuqi Li
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Shupeng Liu
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Dan Liu
- College of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, Liaoning, China
| | - Mengyang Yu
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Xiaofei Wu
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Hongyun Wang
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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4
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Stubberud A, Langseth H, Nachev P, Matharu MS, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024; 44:3331024241268290. [PMID: 39099427 DOI: 10.1177/03331024241268290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.
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Affiliation(s)
- Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, Neuroclinic, StOlav University Hospital, Trondheim, Norway
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5
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Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024; 25:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [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: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
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6
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Bas TG, Duarte V. Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments. Pharmaceuticals (Basel) 2024; 17:925. [PMID: 39065775 PMCID: PMC11279612 DOI: 10.3390/ph17070925] [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: 05/16/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.
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Affiliation(s)
- Tomas Gabriel Bas
- Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile;
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7
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Smaldone AM, Batista VS. Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction. J Chem Theory Comput 2024; 20:4901-4908. [PMID: 38795030 DOI: 10.1021/acs.jctc.4c00432] [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: 05/27/2024]
Abstract
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying O ( n 3 ) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity O ( n 2 ) . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical O ( n 3 ) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.
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Affiliation(s)
- Anthony M Smaldone
- Department of Chemistry, Yale University, New Haven 06511, Connecticut, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven 06511, Connecticut, United States
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8
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Kariya Y, Honma M. Applications of model simulation in pharmacological fields and the problems of theoretical reliability. Drug Metab Pharmacokinet 2024; 56:100996. [PMID: 38797090 DOI: 10.1016/j.dmpk.2024.100996] [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: 11/02/2023] [Revised: 12/23/2023] [Accepted: 12/31/2023] [Indexed: 05/29/2024]
Abstract
The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.
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Affiliation(s)
- Yoshiaki Kariya
- Education Center for Medical Pharmaceutics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Laboratory of Pharmaceutical Regulatory Sciences, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Masashi Honma
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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9
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Wang N, Yang S, Gao Q, Jin X. Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students. Postgrad Med 2024; 136:487-495. [PMID: 38819302 DOI: 10.1080/00325481.2024.2363171] [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: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024]
Abstract
Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.
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Affiliation(s)
- Ning Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Shuo Yang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Qi Gao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Xiuming Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
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10
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Xiang T, Wang J, Li H. Current applications of intestinal organoids: a review. Stem Cell Res Ther 2024; 15:155. [PMID: 38816841 PMCID: PMC11140936 DOI: 10.1186/s13287-024-03768-3] [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: 02/13/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
In the past decade, intestinal organoid technology has paved the way for reproducing tissue or organ morphogenesis during intestinal physiological processes in vitro and studying the pathogenesis of various intestinal diseases. Intestinal organoids are favored in drug screening due to their ability for high-throughput in vitro cultivation and their closer resemblance to patient genetic characteristics. Furthermore, as disease models, intestinal organoids find wide applications in screening diagnostic markers, identifying therapeutic targets, and exploring epigenetic mechanisms of diseases. Additionally, as a transplantable cellular system, organoids have played a significant role in the reconstruction of damaged epithelium in conditions such as ulcerative colitis and short bowel syndrome, as well as in intestinal material exchange and metabolic function restoration. The rise of interdisciplinary approaches, including organoid-on-chip technology, genome editing techniques, and microfluidics, has greatly accelerated the development of organoids. In this review, VOSviewer software is used to visualize hot co-cited journal and keywords trends of intestinal organoid firstly. Subsequently, we have summarized the current applications of intestinal organoid technology in disease modeling, drug screening, and regenerative medicine. This will deepen our understanding of intestinal organoids and further explore the physiological mechanisms of the intestine and drug development for intestinal diseases.
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Affiliation(s)
- Tao Xiang
- Department of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Hui Li
- Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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11
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Khan MK, Raza M, Shahbaz M, Hussain I, Khan MF, Xie Z, Shah SSA, Tareen AK, Bashir Z, Khan K. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem 2024; 12:1408740. [PMID: 38882215 PMCID: PMC11176507 DOI: 10.3389/fchem.2024.1408740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.
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Affiliation(s)
- Mahroza Kanwal Khan
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Mohsin Raza
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Muhammad Shahbaz
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Iftikhar Hussain
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, United States
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
| | - Zhongjian Xie
- Shenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China
| | - Syed Shoaib Ahmad Shah
- Department of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Ayesha Khan Tareen
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
| | - Zoobia Bashir
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Karim Khan
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
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12
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Castilho RM, Castilho LS, Palomares BH, Squarize CH. Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology. Genes (Basel) 2024; 15:710. [PMID: 38927646 PMCID: PMC11202709 DOI: 10.3390/genes15060710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/28/2024] Open
Abstract
This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.
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Affiliation(s)
- Rogerio M. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
| | - Leonard S. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
| | - Bruna H. Palomares
- Oral Diagnosis Department, Piracicaba School of Dentistry, State University of Campinas, Piracicaba 13414-903, Sao Paulo, Brazil;
| | - Cristiane H. Squarize
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
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13
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Zhao B, Xu W, Guan J, Zhou S. Molecular property prediction based on graph structure learning. Bioinformatics 2024; 40:btae304. [PMID: 38710497 PMCID: PMC11112045 DOI: 10.1093/bioinformatics/btae304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/06/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024] Open
Abstract
MOTIVATION Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance. However, current models often ignore relationships between molecules, which could be also helpful for MPP. RESULTS For this sake, in this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular fingerprints, we construct a molecule similarity graph (MSG). Following that, we conduct GSL on the MSG, i.e. molecule-level GSL, to get the final molecular embeddings, which are the results of fuzing both GNN encoded molecular representations and the relationships among molecules. That is, combining both intra-molecule and inter-molecule information. Finally, we use these molecular embeddings to perform MPP. Extensive experiments on 10 various benchmark datasets show that our method could achieve state-of-the-art performance in most cases, especially on classification tasks. Further visualization studies also demonstrate the good molecular representations of our method. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/zby961104/GSL-MPP.
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Affiliation(s)
- Bangyi Zhao
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
| | - Weixia Xu
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
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14
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Tondar A, Sánchez-Herrero S, Bepari AK, Bahmani A, Calvet Liñán L, Hervás-Marín D. Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation. Biomolecules 2024; 14:544. [PMID: 38785951 PMCID: PMC11118195 DOI: 10.3390/biom14050544] [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: 03/23/2024] [Revised: 04/30/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.
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Affiliation(s)
- Abtin Tondar
- Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya (UOC), 08018 Barcelona, Spain;
- Stanford Deep Data Research Center, Department of Genetics, Stanford University, Stanford, CA 94305, USA;
| | - Sergio Sánchez-Herrero
- Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya (UOC), 08018 Barcelona, Spain;
| | - Asim Kumar Bepari
- Department of Pharmaceutical Sciences, North South University (NSU), Dhaka 1229, Bangladesh;
| | - Amir Bahmani
- Stanford Deep Data Research Center, Department of Genetics, Stanford University, Stanford, CA 94305, USA;
| | - Laura Calvet Liñán
- Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona (UAB), Carrer Emprius, 2, 08202 Sabadell, Spain;
| | - David Hervás-Marín
- Department of Applied Statistics, Operational Research, and Quality, Universitat Politècnica de València (UPV), 03801 Alcoy, Spain;
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15
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Canales CSC, Pavan AR, Dos Santos JL, Pavan FR. In silico drug design strategies for discovering novel tuberculosis therapeutics. Expert Opin Drug Discov 2024; 19:471-491. [PMID: 38374606 DOI: 10.1080/17460441.2024.2319042] [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: 11/08/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024]
Abstract
INTRODUCTION Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments. AREAS COVERED In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis. EXPERT OPINION These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.
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Affiliation(s)
- Christian S Carnero Canales
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
- School of Pharmacy, biochemistry and biotechnology, Santa Maria Catholic University, Arequipa, Perú
| | - Aline Renata Pavan
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
| | | | - Fernando Rogério Pavan
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
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16
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Yang W, Wang Y, Han D, Tang W, Sun L. Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery. Biomed Pharmacother 2024; 173:116423. [PMID: 38493593 DOI: 10.1016/j.biopha.2024.116423] [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: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024] Open
Abstract
Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.
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Affiliation(s)
- Weiying Yang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Ye Wang
- School of Life Sciences, Jilin University, Changchun 130012, China
| | - Dongfeng Han
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Wenjing Tang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Lichao Sun
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China.
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17
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Rubinic I, Kurtov M, Rubinic I, Likic R, Dargan PI, Wood DM. Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential. Br J Clin Pharmacol 2024; 90:620-628. [PMID: 37658550 DOI: 10.1111/bcp.15899] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
This paper aims to explore the possibility of employing large language models (LLMs) - a type of artificial intelligence (AI) - in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual-use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual-use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks.
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Affiliation(s)
- Igor Rubinic
- University of Rijeka School of Medicine, Rijeka, Croatia
- Clinical Hospital Centre Rijeka, Rijeka, Croatia
| | | | - Ivan Rubinic
- School of Engineering, University of Rijeka, Rijeka, Croatia
| | - Robert Likic
- University of Zagreb School of Medicine, Zagreb, Croatia
- Clinical Hospital Centre Zagreb, Zagreb, Croatia
| | - Paul I Dargan
- Faculty of Life Sciences and Medicine, King's College London, London, UK
- Clinical Toxicology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - David M Wood
- Faculty of Life Sciences and Medicine, King's College London, London, UK
- Clinical Toxicology, Guy's and St Thomas' NHS Foundation Trust, London, UK
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18
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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [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: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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19
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Koh CMM, Ping LSY, Xuan CHH, Theng LB, San HS, Palombo EA, Wezen XC. A data-driven machine learning approach for discovering potent LasR inhibitors. Bioengineered 2023; 14:2243416. [PMID: 37552115 PMCID: PMC10411317 DOI: 10.1080/21655979.2023.2243416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
The rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.
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Affiliation(s)
- Christabel Ming Ming Koh
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Lilian Siaw Yung Ping
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Christopher Ha Heng Xuan
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Lau Bee Theng
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Hwang Siaw San
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
| | - Enzo A. Palombo
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Xavier Chee Wezen
- Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia
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20
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Lu H, Wei Z, Wang X, Zhang K, Liu H. GraphGPT: A Graph Enhanced Generative Pretrained Transformer for Conditioned Molecular Generation. Int J Mol Sci 2023; 24:16761. [PMID: 38069085 PMCID: PMC10706000 DOI: 10.3390/ijms242316761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/16/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Condition-based molecular generation can generate a large number of molecules with particular properties, expanding the virtual drug screening library, and accelerating the process of drug discovery. In this study, we combined a molecular graph structure and sequential representations using a generative pretrained transformer (GPT) architecture for generating molecules conditionally. The incorporation of graph structure information facilitated a better comprehension of molecular topological features, and the augmentation of a sequential contextual understanding of GPT architecture facilitated molecular generation. The experiments indicate that our model efficiently produces molecules with the desired properties, with valid and unique metrics that are close to 100%. Faced with the typical task of generating molecules based on a scaffold in drug discovery, our model is able to preserve scaffold information and generate molecules with low similarity and specified properties.
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Affiliation(s)
| | | | | | | | - Hao Liu
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
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21
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Handa T. The potential role of artificial intelligence in the clinical practice of interstitial lung disease. Respir Investig 2023; 61:702-710. [PMID: 37708636 DOI: 10.1016/j.resinv.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/26/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.
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Affiliation(s)
- Tomohiro Handa
- Department of Advanced Medicine for Respiratory Failure and Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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22
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Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician. J Infect 2023; 87:287-294. [PMID: 37468046 DOI: 10.1016/j.jinf.2023.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection. OBJECTIVES We summarise recent and potential future applications of AI and its relevance to clinical infection practice. METHODS 1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions. RESULTS There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias. CONCLUSIONS Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.
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Affiliation(s)
- Anastasia A Theodosiou
- Clinical and Experimental Sciences and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Tremona Road, SO166YD Southampton, United Kingdom.
| | - Robert C Read
- Clinical and Experimental Sciences and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Tremona Road, SO166YD Southampton, United Kingdom
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23
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Cools R, Kerkhofs K, Leitao RCF, Bormans G. Preclinical Evaluation of Novel PET Probes for Dementia. Semin Nucl Med 2023; 53:599-629. [PMID: 37149435 DOI: 10.1053/j.semnuclmed.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/24/2023] [Indexed: 05/08/2023]
Abstract
The development of novel PET imaging agents that selectively bind specific dementia-related targets can contribute significantly to accurate, differential and early diagnosis of dementia causing diseases and support the development of therapeutic agents. Consequently, in recent years there has been a growing body of literature describing the development and evaluation of potential new promising PET tracers for dementia. This review article provides a comprehensive overview of novel dementia PET probes under development, classified by their target, and pinpoints their preclinical evaluation pathway, typically involving in silico, in vitro and ex/in vivo evaluation. Specific target-associated challenges and pitfalls, requiring extensive and well-designed preclinical experimental evaluation assays to enable successful clinical translation and avoid shortcomings observed for previously developed 'well-established' dementia PET tracers are highlighted in this review.
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Affiliation(s)
- Romy Cools
- Laboratory for Radiopharmaceutical Research, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Kobe Kerkhofs
- Laboratory for Radiopharmaceutical Research, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium; NURA, Belgian Nuclear Research Centre (SCK CEN), Mol, Belgium
| | - Renan C F Leitao
- Laboratory for Radiopharmaceutical Research, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Guy Bormans
- Laboratory for Radiopharmaceutical Research, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
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24
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Foka FET, Mufhandu HT. Current ARTs, Virologic Failure, and Implications for AIDS Management: A Systematic Review. Viruses 2023; 15:1732. [PMID: 37632074 PMCID: PMC10458198 DOI: 10.3390/v15081732] [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: 06/30/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Antiretroviral therapies (ARTs) have revolutionized the management of human immunodeficiency virus (HIV) infection, significantly improved patient outcomes, and reduced the mortality rate and incidence of acquired immunodeficiency syndrome (AIDS). However, despite the remarkable efficacy of ART, virologic failure remains a challenge in the long-term management of HIV-infected individuals. Virologic failure refers to the persistent detectable viral load in patients receiving ART, indicating an incomplete suppression of HIV replication. It can occur due to various factors, including poor medication adherence, drug resistance, suboptimal drug concentrations, drug interactions, and viral factors such as the emergence of drug-resistant strains. In recent years, extensive efforts have been made to understand and address virologic failure in order to optimize treatment outcomes. Strategies to prevent and manage virologic failure include improving treatment adherence through patient education, counselling, and supportive interventions. In addition, the regular monitoring of viral load and resistance testing enables the early detection of treatment failure and facilitates timely adjustments in ART regimens. Thus, the development of novel antiretroviral agents with improved potency, tolerability, and resistance profiles offers new options for patients experiencing virologic failure. However, new treatment options would also face virologic failure if not managed appropriately. A solution to virologic failure requires a comprehensive approach that combines individualized patient care, robust monitoring, and access to a range of antiretroviral drugs.
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Affiliation(s)
- Frank Eric Tatsing Foka
- Department of Microbiology, Virology Laboratory, School of Biological Sciences, Faculty of Natural and Agricultural Sciences, North West University, Mafikeng, Private Bag, Mmabatho X2046, South Africa
| | - Hazel Tumelo Mufhandu
- Department of Microbiology, Virology Laboratory, School of Biological Sciences, Faculty of Natural and Agricultural Sciences, North West University, Mafikeng, Private Bag, Mmabatho X2046, South Africa
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25
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Baldo P, De Re V, Garutti M. How will the identification and therapeutic intervention of genetic targets in oncology evolve for future therapy? Expert Opin Ther Targets 2023; 27:1189-1194. [PMID: 38095918 DOI: 10.1080/14728222.2023.2295493] [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: 07/23/2023] [Accepted: 12/12/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Mapping of the human genome, together with the broad understanding of new biomolecular pathways involved in cancer development, represents a huge dividing line for advances in cancer treatment. This special article aims to express the next evolution of cancer therapy, while also considering the challenges and uncertainties facing future directions. AREA COVERED The recent achievements of medical science in the oncology field concern both new diagnostic techniques, such as liquid biopsy, and therapeutic strategies with innovative anticancer drugs. Although several molecular characteristics of tumors are linked to the tissue of origin, some mutations are shared by multiple tumor histologies, thus paving the way for what is called 'precision oncology.' The article highlights the importance of identifying new mutations and biomolecular pathways that can be pursued with new anticancer drugs. EXPERT OPINION Oncology and medical science have made great progress in understanding new molecular targets; being able to early identify tumor markers that are not confined to a single organ through minimally invasive diagnostic techniques allows us to design new effective therapeutic strategies. Multidisciplinary teams now aim to evaluate the most appropriate and personalized diagnostic/therapeutic approach for the individual patient.
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Affiliation(s)
- Paolo Baldo
- Hospital Pharmacy Unit, Centro di Riferimento Oncologico di Aviano, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Valli De Re
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Mattia Garutti
- Department of Medical Oncology, Centro di Riferimento Oncologico di Aviano, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
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26
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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27
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Liu Y, Yang Q, Chen S, Li Z, Fu L. Targeting VPS34 in autophagy: An update on pharmacological small-molecule compounds. Eur J Med Chem 2023; 256:115467. [PMID: 37178482 DOI: 10.1016/j.ejmech.2023.115467] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/19/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
VPS34 is well-known to be the unique member of the class III phosphoinositide 3-kinase (PI3K) family, forming VPS34 complex 1 and complex 2, which are involved in several key physiological processes. Of note, VPS34 complex 1 is an important node of autophagosome generation, which controls T cell metabolism and maintains cellular homeostasis through the autophagic pathway. And, VPS34 complex 2 is involved in endocytosis as well as vesicular transport, and is closely related to neurotransmission, antigen presentation and brain development. Due to the two important biological functions of VPS34, its dysregulation can lead to the development of cardiovascular disease, cancer, neurological disorders, and many types of human diseases by altering normal human physiology. Thus, in this review, we not only summarize the molecular structure and function of VPS34, but demonstrate the relationships between VPS34 and human diseases. Moreover, we further discuss the current small molecule inhibitors targeting VPS34 based upon the structure and function of VPS34, which may provide an insight into the future targeted drug development.
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Affiliation(s)
- Yuan Liu
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Qilin Yang
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Siwei Chen
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Zixiang Li
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Leilei Fu
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
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28
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Sippl W. Editorial for Special Issue—“Early-Stage Drug Discovery: Advances and Challenges”. Int J Mol Sci 2023; 24:ijms24076516. [PMID: 37047488 PMCID: PMC10094839 DOI: 10.3390/ijms24076516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The development of a new drug from the first hit to the launch of an approved product is a complex process that usually take around 12–15 years and costs more than USD 1–2 billion [...]
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Affiliation(s)
- Wolfgang Sippl
- Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, Kurt-Mothes-Str. 3, 06122 Halle, Germany
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29
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Cortat Y, Nedyalkova M, Schindler K, Kadakia P, Demirci G, Nasiri Sovari S, Crochet A, Salentinig S, Lattuada M, Steiner OM, Zobi F. Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents. Antibiotics (Basel) 2023; 12:antibiotics12030619. [PMID: 36978486 PMCID: PMC10044843 DOI: 10.3390/antibiotics12030619] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a 'scaffold-hopping' approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2'-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of 'scaffold-hopping' for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.
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Affiliation(s)
- Youri Cortat
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Miroslava Nedyalkova
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Kevin Schindler
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Parth Kadakia
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Gozde Demirci
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Sara Nasiri Sovari
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Aurelien Crochet
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Stefan Salentinig
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Marco Lattuada
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Olimpia Mamula Steiner
- Haute école d'Ingénierie et d'Architecture, University of Applied Sciences Western Switzerland HES-SO, Pérolles 80, 1700 Fribourg, Switzerland
| | - Fabio Zobi
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
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