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Yucel MA, Adal E, Aktekin MB, Hepokur C, Gambacorta N, Nicolotti O, Algul O. From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors. ChemMedChem 2024:e202400108. [PMID: 38726553 DOI: 10.1002/cmdc.202400108] [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: 02/05/2024] [Revised: 04/24/2024] [Indexed: 07/21/2024]
Abstract
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.
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Affiliation(s)
- Mehmet Ali Yucel
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
| | - Ercan Adal
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Mine Buga Aktekin
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Ceylan Hepokur
- Department of Biochemistry, Faculty of Pharmacy, Sivas Cumhuriyet University, 58140, Sivas, Türkiye
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Oztekin Algul
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
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2
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [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: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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3
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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [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: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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4
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Liu CL, Jiang Y, Li HJ. Quality Consistency Evaluation of Traditional Chinese Medicines: Current Status and Future Perspectives. Crit Rev Anal Chem 2024:1-18. [PMID: 38252135 DOI: 10.1080/10408347.2024.2305267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Quality consistency evaluation of traditional Chinese medicines (TCMs) is a crucial factor that determines the safe and effective application in clinical settings. However, TCMs exhibit diverse, heterogeneous, complex, and flexible chemical compositions, as well as variability in preparation processes. These characteristics pose greater challenges in researching the consistency of TCMs compared to chemically synthesized and biological drugs. Therefore, it is paramount to develop effective strategies for evaluating the quality consistency of TCMs. From the starting point of quality properties, this review explores the strategy used to evaluate quality consistency in terms of chemistry-based strategy (chemical consistency) and the biology-based strategy (bioequivalence). Among them, the chemistry-based strategy is the mainstream, and biology-based strategy complements the chemistry-based strategy each other. Furthermore, the emerging chemistry-biology strategies (overall evaluation) is discussed, including individually combining strategy and integration strategy. Finally, this review provides insights into the challenges and future perspectives in this field. By highlighting current status and trends in TCMs quality consistency, this review aims to contribute to establishment of generally applicable chemistry-biology integrated evaluation strategy for TCMs. This will facilitate the advancement toward a higher stage of overall quality evaluation.
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Affiliation(s)
- Chun-Lu Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Yan Jiang
- College of Chemical Engineering, Nanjing Forestry University, Nanjing, China
| | - Hui-Jun Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
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5
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Song HJ, Park YJ, Jeong HY, Kim BG, Kim JH, Im YG. Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1293. [PMID: 37512104 PMCID: PMC10385577 DOI: 10.3390/medicina59071293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study collected 175 clinical photographic images of the dorsal tongue surface, which were divided into 7782 cropped images classified into normal, abnormal, and non-tongue regions and trained using the VGG16 deep learning model. The 80 photographic images of the entire dorsal tongue surface were used for the segmentation of abnormal regions using point mapping segmentation. Results: The F1-scores of the abnormal and normal classes were 0.960 (precision: 0.935, recall: 0.986) and 0.968 (precision: 0.987, recall: 0.950), respectively, in the prediction of the VGG16 model. As a result of evaluation using point mapping segmentation, the average F1-scores were 0.727 (precision: 0.717, recall: 0.737) and 0.645 (precision: 0.650, recall: 0.641), the average intersection of union was 0.695 and 0.590, and the average precision was 0.940 and 0.890, respectively, for abnormal and normal classes. Conclusions: The deep learning algorithm used in this study can accurately determine abnormal areas on the dorsal tongue surface, which can assist in diagnosing specific diseases or conditions of the tongue mucosa.
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Affiliation(s)
- Ho-Jun Song
- Department of Dental Materials, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Yeong-Joon Park
- Department of Dental Materials, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Hie-Yong Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Byung-Gook Kim
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Jae-Hyung Kim
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Yeong-Gwan Im
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
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6
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Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. Eur J Pharmacol 2022; 933:175260. [PMID: 36116517 DOI: 10.1016/j.ejphar.2022.175260] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022]
Abstract
It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.
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Affiliation(s)
- Dongna Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jing Hu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lin Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lili Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Qingsheng Yin
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jiangwei Shi
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China
| | - Hong Guo
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yanjun Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China.
| | - Pengwei Zhuang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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7
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Water Extract of Piper longum Linn Ameliorates Ovariectomy-Induced Bone Loss by Inhibiting Osteoclast Differentiation. Nutrients 2022; 14:nu14173667. [PMID: 36079923 PMCID: PMC9459790 DOI: 10.3390/nu14173667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/23/2022] Open
Abstract
Piper longum linn has traditionally been used for the treatment of respiratory and gastrointestinal disorders in India. Although various pharmacological effects of P. longum have been studied, its effects on bone have not been clearly elucidated. Therefore, this study examined the inhibitory effect of the water extract of P. longum Linn (WEPL) on osteoclast differentiation. WEPL directly affected the osteoclast precursors and suppressed osteoclast differentiation in vitro. In addition, the expression levels of c-Fos and nuclear factor of activated T cells 1, a critical transcription factor for osteoclastogenesis, were significantly downregulated by WEPL via the suppression of the receptor activator of nuclear factor (NF)-κB ligand-induced mitogen-activated protein kinase and NF-κB signaling pathways. Consistent with the in vitro results, oral administration of WEPL (100 and 300 mpk) to ovariectomized mice for six weeks relieved the OVX-induced bone loss. We also identified phytochemicals in WEPL that are reported to exert inhibitory effects on osteoclastogenesis and/or bone loss. Collectively, the findings of our study indicate that WEPL has an anti-osteoporotic effect on OVX-induced bone loss by diminishing osteoclast differentiation, suggesting that it may be useful to treat several bone diseases caused by excessive bone resorption.
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8
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Jia XN, Wang WJ, Yin B, Zhou LJ, Zhen YQ, Zhang L, Zhou XL, Song HN, Tang Y, Gao F. Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity. ACS OMEGA 2022; 7:28334-28341. [PMID: 35990425 PMCID: PMC9386835 DOI: 10.1021/acsomega.2c02854] [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: 05/08/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.
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Affiliation(s)
- Xiao-Nan Jia
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Wei-Jia Wang
- School
of Computer Science and Engineering, University
of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Bo Yin
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Lin-Jing Zhou
- School
of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Yong-Qi Zhen
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Lan Zhang
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Xian-Li Zhou
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
| | - Hai-Ning Song
- Department
of Pharmacy, The Third People’s Hospital of Chengdu and College
of Medicine, Southwest Jiaotong University, Chengdu 610031, PR China
| | - Yong Tang
- School
of Computer Science and Engineering, University
of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Feng Gao
- School
of Life Science and Engineering, Southwest
Jiaotong University, Chengdu 610031, PR China
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9
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Artificial intelligence in virtual screening: models versus experiments. Drug Discov Today 2022; 27:1913-1923. [PMID: 35597513 DOI: 10.1016/j.drudis.2022.05.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/08/2022] [Accepted: 05/12/2022] [Indexed: 12/22/2022]
Abstract
A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.
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10
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Tao Xue H, Stanley-Baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discov Today 2022; 27:2235-2243. [DOI: 10.1016/j.drudis.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
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11
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Du BX, Qin Y, Jiang YF, Xu Y, Yiu SM, Yu H, Shi JY. Compound–protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discov Today 2022; 27:1350-1366. [DOI: 10.1016/j.drudis.2022.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/19/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022]
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12
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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13
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Alves LA, Ferreira NCDS, Maricato V, Alberto AVP, Dias EA, Jose Aguiar Coelho N. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs. Front Chem 2022; 9:787194. [PMID: 35127645 PMCID: PMC8811035 DOI: 10.3389/fchem.2021.787194] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022] Open
Abstract
Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.
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Affiliation(s)
- Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
- *Correspondence: Luiz Anastacio Alves,
| | | | - Victor Maricato
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | | | - Evellyn Araujo Dias
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | - Nt Jose Aguiar Coelho
- National Institute of Industrial Property - INPI and Veiga de Almeida University - UVA, Rio de Janeiro, Brazil
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14
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Vijayan RSK, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today 2021; 27:967-984. [PMID: 34838731 DOI: 10.1016/j.drudis.2021.11.023] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/15/2021] [Accepted: 11/19/2021] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.
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Affiliation(s)
- R S K Vijayan
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, USA
| | - Jan Kihlberg
- Department of Chemistry-BMC, Uppsala University, Uppsala, Sweden
| | - Jason B Cross
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, USA.
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15
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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