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Guo Q, Fu B, Tian Y, Xu S, Meng X. Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. Curr Med Res Opin 2024; 40:1483-1493. [PMID: 39083361 DOI: 10.1080/03007995.2024.2387187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024]
Abstract
Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.
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Affiliation(s)
- Qi Guo
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Bo Fu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Yuan Tian
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Shujun Xu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Xin Meng
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
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2
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Diaz-Pier S, Carloni P. Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives. Curr Opin Struct Biol 2024; 87:102817. [PMID: 38795562 DOI: 10.1016/j.sbi.2024.102817] [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: 12/19/2023] [Accepted: 03/27/2024] [Indexed: 05/28/2024]
Abstract
New high-performance computing architectures are becoming operative, in addition to exascale computers. Quantum computers (QC) solve optimization problems with unprecedented efficiency and speed, while neuromorphic hardware (NMH) simulates neural network dynamics. Albeit, at the moment, both find no practical use in all atom biomolecular simulations, QC might be exploited in the not-too-far future to simulate systems for which electronic degrees of freedom play a key and intricate role for biological function, whereas NMH might accelerate molecular dynamics simulations with low energy consumption. Machine learning and artificial intelligence algorithms running on NMH and QC could assist in the analysis of data and speed up research. If these implementations are successful, modular supercomputing could further dramatically enhance the overall computing capacity by combining highly optimized software tools into workflows, linking these architectures to exascale computers.
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Affiliation(s)
- Sandra Diaz-Pier
- Simulation & Data Lab Neuroscience, Institute for Advanced Simulations IAS-5, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, JARA, 52428 Jülich, Germany.
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, JARA, 52428 Jülich, Germany
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3
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Duo L, Liu Y, Ren J, Tang B, Hirst JD. Artificial intelligence for small molecule anticancer drug discovery. Expert Opin Drug Discov 2024; 19:933-948. [PMID: 39074493 DOI: 10.1080/17460441.2024.2367014] [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: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.
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Affiliation(s)
- Lihui Duo
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Yu Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jianfeng Ren
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Bencan Tang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park, Nottingham, UK
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Li Y, Peng HQ, Wen ML, Yang LQ. Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics. Molecules 2024; 29:3614. [PMID: 39125019 PMCID: PMC11314057 DOI: 10.3390/molecules29153614] [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: 06/20/2024] [Revised: 07/19/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.
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Affiliation(s)
- Yi Li
- College of Mathematics and Computer Science, Dali University, Dali 671000, China; (Y.L.); (H.-Q.P.)
- College of Agriculture and Biological Science, Dali University, Dali 671000, China
| | - Hong-Qian Peng
- College of Mathematics and Computer Science, Dali University, Dali 671000, China; (Y.L.); (H.-Q.P.)
| | - Meng-Liang Wen
- School of Life Science, Yunnan University, Kunming 650091, China
| | - Li-Quan Yang
- College of Agriculture and Biological Science, Dali University, Dali 671000, China
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Li J, Dong X, Zhang Y. Probing the interaction between the metallo-β-lactamase SMB-1 and ampicillin by multispectral approaches combined with molecular dynamics. J Mol Recognit 2024:e3100. [PMID: 39014869 DOI: 10.1002/jmr.3100] [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: 04/18/2024] [Revised: 06/22/2024] [Accepted: 07/02/2024] [Indexed: 07/18/2024]
Abstract
Metallo-β-lactamases (MβLs) hydrolyze and inactivate β-lactam antibiotics, are a pivotal mechanism conferring resistance against bacterial infections. SMB-1, a novel B3 subclass of MβLs from Serratia marcescens could deactivate almost all β-lactam antibiotics including ampicillin (AMP), which has posed a serious threat to public health. To illuminate the mechanism of recognition and interaction between SMB-1 and AMP, various fluorescence spectroscopy techniques and molecular dynamics simulation were employed. The results of quenching spectroscopy unraveled that AMP could make SMB-1 fluorescence quenching that mechanism was the static quenching; the synchronous and three-dimensional fluorescence spectra validated that the microenvironment and conformation of SMB-1 were altered after interaction with AMP. The molecular dynamics results demonstrated that the whole AMP enters the binding pocket of SMB-1, even though with a relatively bulky R1 side chain. Loop1 and loop2 in SMB-1 undergo significant fluctuations, and α2 (71-73) and local α5 (186-188) were turned into random coils, promoting zinc ion exposure consistent with circular dichroism spectroscopy results. The binding between them was driven by a combination of enthalpy and entropy changes, which was dominated by electrostatic force in agreement with the fluorescence observations. The present study brings structural insights and solid foundations for the design of new substrates for β-lactamases and the development of effective antibiotics that are resistant to superbugs.
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Affiliation(s)
- Jiachen Li
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, China
| | - Xiaoting Dong
- Institute of Basic and Translational Medicine, Xi'an Medical University, Xi'an, China
| | - Yeli Zhang
- College of Biological Sciences and Technology, Taiyuan Normal University, Jinzhong, China
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Nguyen ATN, Nguyen DTN, Koh HY, Toskov J, MacLean W, Xu A, Zhang D, Webb GI, May LT, Halls ML. The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. Br J Pharmacol 2024; 181:2371-2384. [PMID: 37161878 DOI: 10.1111/bph.16140] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/14/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023] Open
Abstract
The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.
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Affiliation(s)
- Anh T N Nguyen
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Diep T N Nguyen
- Department of Information Technology, Faculty of Engineering and Technology, Vietnam National University, Cau Giay, Hanoi, Vietnam
| | - Huan Yee Koh
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Jason Toskov
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - William MacLean
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Andrew Xu
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Daokun Zhang
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Geoffrey I Webb
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Lauren T May
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Michelle L Halls
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
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Rao P, Ninama J, Dudhat M, Goswami D, Rawal RM. Curcumin interferes with chitin synthesis in Aedes aegypti: a computational and experimental investigation. Mol Divers 2024; 28:1505-1529. [PMID: 37358753 DOI: 10.1007/s11030-023-10672-0] [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: 05/09/2023] [Accepted: 06/10/2023] [Indexed: 06/27/2023]
Abstract
Throughout history, vector-borne diseases have consistently posed significant challenges to human health. Among the strategies for vector control, chemical insecticides have seen widespread use since their inception. Nevertheless, their effectiveness is continually undermined by the steady growth of insecticide resistance within these vector populations. As such, the demand for more robust, efficient, and cost-effective natural insecticides has become increasingly pressing. One promising avenue of research focuses on chitin, a crucial structural component of mosquitoes' exoskeletons and other insects. Chitin not only provides protection and rigidity but also lends flexibility to the insect body. It undergoes substantial transformations during insect molting, a process known as ecdysis. Crucially, the production of chitin is facilitated by an enzyme known as chitin synthase, making it an attractive target for potential novel insecticides. Our recent study delved into the impacts of curcumin, a natural derivative of turmeric, on chitin synthesis and larval development in Aedes aegypti, a mosquito species known to transmit dengue and yellow fever. Our findings demonstrate that even sub-lethal amounts of curcumin can significantly reduce overall chitin content and disrupt the cuticle development in the 4th instar larvae of Aedes aegypti. Further to this, we utilized computational analyses to investigate how curcumin interacts with chitin synthase. Techniques such as molecular docking, pharmacophore feature mapping, and molecular dynamics (MD) simulations helped to illustrate that curcumin binds to the same site as polyoxin D, a recognized inhibitor of chitin synthase. These findings point to curcumin's potential as a natural, bioactive larvicide that targets chitin synthase in mosquitoes and potentially other insects.
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Affiliation(s)
- Priyashi Rao
- Department of Biochemistry & Forensic Science, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India
| | - Jinal Ninama
- Department of Biochemistry & Forensic Science, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India
| | - Mansi Dudhat
- Department of Biochemistry & Forensic Science, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India
| | - Dweipayan Goswami
- Department of Microbiology & Biotechnology, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India
| | - Rakesh M Rawal
- Department of Biochemistry & Forensic Science, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India.
- Department of Life Science, University School of Sciences, Gujarat University, Ahmedabad, Gujarat, 380009, India.
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Savva K, Zachariou M, Bourdakou MM, Dietis N, Spyrou GM. D Re Amocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing. Int J Mol Sci 2024; 25:5319. [PMID: 38791356 PMCID: PMC11121186 DOI: 10.3390/ijms25105319] [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: 03/21/2024] [Revised: 04/26/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.
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Affiliation(s)
- Kyriaki Savva
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Marilena M. Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Nikolas Dietis
- Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Nicosia 2115, Cyprus;
| | - George M. Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
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Benarous K, Serseg T, Mermer A, Tahmasebifar A, Boulebd H, Linani A. Exploring the Anti-Cancer Potential of Hispidin: A Comprehensive in Silico and in Vitro Study on Human Osteosarcoma Saos2 Cells. Chem Biodivers 2024; 21:e202301833. [PMID: 38456582 DOI: 10.1002/cbdv.202301833] [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: 11/16/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 03/09/2024]
Abstract
Hispidin was initially discovered in basidiomycete Inonotus hispidus (Bull.) P. Karst and this extraordinary compound possesses immense potency and can be extracted from the wild mushroom through specialized bioreactor cultivation techniques. In our study, we isolated it from Inonotus hispidus (Bull.) P. Karst., with a yield of 3.6 %. We identified and characterized hispidin through the implementation of spectroscopic techniques such as FTIR, NMR, and MS. Additionally, we utilized Thermogravimetric Analysis for thermal characterization of the compound. Computational studies based on DFT were performed to investigate the molecular structure, electronic properties, and chemical reactivity of hispidin. PASS analysis for hispidin demonstrated that 19 of them are anti-neoplastic activities. The Pharmacology prediction of hispidin confirm that it is not toxic, non-carcinogenesis with a good human intestinal absorption. The effect of hispidin on the viability of bone cancer cells was evaluated by MTT assay. The results showed that hispidin significantly reduced SaoS2 cell viability in a dose-dependent manner. Molecular docking was carried out using five targets related to bone cancer to determine the interactions between hispidin and the studied proteins. The results demonstrate that hispidin is a good inhibitor for the five targets. Dynamic simulation shows a good stability of the complex hispidin-protein.
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Affiliation(s)
- Khedidja Benarous
- Fundamental sciences laboratory, Amar Telidji University, Laghouat, Algeria
| | - Talia Serseg
- Fundamental sciences laboratory, Amar Telidji University, Laghouat, Algeria
- Laboratoire de sciences appliquées et didactiques, Ecole Normale Supérieure de, Laghouat, Algeria
| | - Arif Mermer
- Department of Biotechnology, University of Health Sciences, İstanbul, Türkiye
- Experimental Medicine Application and Research Center, Validebag Research Park, University of Health Sciences, İstanbul, Türkiye
| | - Aydin Tahmasebifar
- Department of Biomaterials, University of Health Sciences, İstanbul, Türkiye
- Experimental Medicine Application and Research Center, Validebag Research Park, University of Health Sciences, İstanbul, Türkiye
| | - Houssem Boulebd
- Chemistry Department, Faculty of Exact Science, University of Constantine 1, Constantine, 25000, Algeria
| | - Abderahmane Linani
- Fundamental sciences laboratory, Amar Telidji University, Laghouat, Algeria
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Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 2024; 22:411. [PMID: 38702711 PMCID: PMC11069149 DOI: 10.1186/s12967-024-05067-0] [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: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/06/2024] Open
Abstract
Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.
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Affiliation(s)
- Claudio Carini
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, New Hunt's House, King's College London, Guy's Campus, London, UK.
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD, USA.
| | - Attila A Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI, USA.
- Legorreta Cancer Center at Brown University, Providence, RI, USA.
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11
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [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] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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Jones J, Clark RD, Lawless MS, Miller DW, Waldman M. The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations. J Comput Aided Mol Des 2024; 38:14. [PMID: 38499823 DOI: 10.1007/s10822-024-00552-6] [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: 08/16/2023] [Accepted: 02/13/2024] [Indexed: 03/20/2024]
Abstract
Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.
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Affiliation(s)
- Jeremy Jones
- Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA.
| | - Robert D Clark
- The Indiana University Luddy School of Informatics, Computing and Engineering, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA
| | - Michael S Lawless
- Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA
| | - David W Miller
- Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA
| | - Marvin Waldman
- Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA
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13
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Xu X, Zhou J, Zhu C, Zhan Q, Li Z, Zhang R, Wang Y, Liao X, Gao X. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning. F1000Res 2024; 12:757. [PMID: 38434657 PMCID: PMC10905145 DOI: 10.12688/f1000research.130936.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
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Affiliation(s)
- Xiaopeng Xu
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juexiao Zhou
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Chen Zhu
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Qing Zhan
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Zhongxiao Li
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | | | - Yu Wang
- Syneron Technology, Guangzhou, China
| | - Xingyu Liao
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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14
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [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: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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15
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Bertani M, Charpentier T, Faglioni F, Pedone A. Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses. J Chem Theory Comput 2024. [PMID: 38217496 DOI: 10.1021/acs.jctc.3c01115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [Wang, H. Comput. Phys. Commun. 2018, 228, 178-184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.
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Affiliation(s)
- Marco Bertani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | | | - Francesco Faglioni
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Alfonso Pedone
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
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16
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He B, Guo J, Tong HHY, To WM. Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review. Mini Rev Med Chem 2024; 24:1353-1367. [PMID: 38243944 DOI: 10.2174/0113895575271267231123160503] [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: 07/09/2023] [Revised: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 01/22/2024]
Abstract
Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.
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Affiliation(s)
- Baoyu He
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Jingjing Guo
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Henry H Y Tong
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Wai Ming To
- Faculty of Business, Macao Polytechnic University, Macao, China
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17
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Wu Z, Wu Y, Zhu C, Wu X, Zhai S, Wang X, Su Z, Duan H. Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides. J Chem Inf Model 2023; 63:7655-7668. [PMID: 38049371 DOI: 10.1021/acs.jcim.3c01385] [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: 12/06/2023]
Abstract
The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.
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Affiliation(s)
- Zhipeng Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yejian Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Cheng Zhu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinyi Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Silong Zhai
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinqiao Wang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zhihao Su
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
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18
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Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; 84:1652-1663. [PMID: 37712494 DOI: 10.1002/ddr.22115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/14/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.
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Affiliation(s)
- Prafulla C Tiwari
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Rishi Pal
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Manju J Chaudhary
- Department of Physiology, Government Medical College, Kannauj, Uttar Pradesh, India
| | - Rajendra Nath
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
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19
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Guo W, Lv C, Guo M, Zhao Q, Yin X, Zhang L. Innovative applications of artificial intelligence in zoonotic disease management. SCIENCE IN ONE HEALTH 2023; 2:100045. [PMID: 39077042 PMCID: PMC11262289 DOI: 10.1016/j.soh.2023.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/22/2023] [Indexed: 07/31/2024]
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
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Affiliation(s)
- Wenqiang Guo
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chenrui Lv
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Guo
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
| | - Qiwei Zhao
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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20
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Dragan P, Joshi K, Atzei A, Latek D. Keras/TensorFlow in Drug Design for Immunity Disorders. Int J Mol Sci 2023; 24:15009. [PMID: 37834457 PMCID: PMC10573944 DOI: 10.3390/ijms241915009] [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: 09/08/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.
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Affiliation(s)
- Paulina Dragan
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-903 Warsaw, Poland; (P.D.); (A.A.)
| | - Kavita Joshi
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-903 Warsaw, Poland; (P.D.); (A.A.)
| | - Alessandro Atzei
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-903 Warsaw, Poland; (P.D.); (A.A.)
- Department of Life and Environmental Science, Food Toxicology Unit, University of Cagliari, University Campus of Monserrato, SS 554, 09042 Cagliari, Italy
| | - Dorota Latek
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-903 Warsaw, Poland; (P.D.); (A.A.)
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21
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Lefin N, Herrera-Belén L, Farias JG, Beltrán JF. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol Divers 2023:10.1007/s11030-023-10718-3. [PMID: 37626205 DOI: 10.1007/s11030-023-10718-3] [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: 05/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
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Affiliation(s)
- Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomás, Temuco, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
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22
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Lu W, Lee NA, Buehler MJ. Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing. Proc Natl Acad Sci U S A 2023; 120:e2305273120. [PMID: 37487072 PMCID: PMC10401013 DOI: 10.1073/pnas.2305273120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/09/2023] [Indexed: 07/26/2023] Open
Abstract
Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.
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Affiliation(s)
- Wei Lu
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Nic A. Lee
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA02139
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
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23
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Xing C, Chen P, Zhang L. Computational insight into stability-enhanced systems of anthocyanin with protein/peptide. FOOD CHEMISTRY. MOLECULAR SCIENCES 2023; 6:100168. [PMID: 36923156 PMCID: PMC10009195 DOI: 10.1016/j.fochms.2023.100168] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/24/2022] [Accepted: 02/18/2023] [Indexed: 02/24/2023]
Abstract
Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of plants native to South and Central America. However, these pigments are unstable under conditions of varying pH, heat, etc., which limits their potential applications. One method for preserving the stability of anthocyanins is through encapsulation using proteins or peptides. Nevertheless, the complex and diverse structure of these molecules, as well as the limitation of experimental technologies, have hindered a comprehensive understanding of the encapsulation processes and the mechanisms by which stability is enhanced. To address these challenges, computational methods, such as molecular docking and molecular dynamics simulation have been used to study the binding affinity and dynamics of interactions between proteins/peptides and anthocyanins. This review summarizes the mechanisms of interaction between these systems, based on computational approaches, and highlights the role of proteins and peptides in the stability enhancement of anthocyanins. It also discusses the current limitations of these methods and suggests possible solutions.
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Affiliation(s)
- Cheng Xing
- Department of Chemical Engineering and Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
- School of Science, Beijing Jiaotong University, 100044 Beijing, China
| | - P. Chen
- Department of Chemical Engineering and Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
| | - Lei Zhang
- Department of Chemical Engineering and Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
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24
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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25
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Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel) 2023; 16:891. [PMID: 37375838 DOI: 10.3390/ph16060891] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.
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Affiliation(s)
- Alexandre Blanco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Daniel Conde-Torres
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Rebeca Garcia-Fandino
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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27
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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28
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Muniyappan S, Rayan AXA, Varrieth GT. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9530-9571. [PMID: 37161255 DOI: 10.3934/mbe.2023419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
MOTIVATION In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). METHODS In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. RESULTS The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.
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Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, India
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29
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Li Q, Wang H, Yang WL, Yang JK. An approach combining deep learning and molecule docking for drug discovery of cathepsin L. Expert Opin Drug Discov 2023; 18:347-356. [PMID: 36852432 DOI: 10.1080/17460441.2023.2174522] [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: 03/01/2023]
Abstract
OBJECTIVES Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors. METHODS The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations. RESULTS The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins. CONCLUSION Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.
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Affiliation(s)
- Qi Li
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Hao Wang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wei-Li Yang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jin-Kui Yang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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30
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Duran S, Moin ST. Structure and dynamics of 5-lipoxygenase and its complexes - a molecular dynamics simulation study. J Mol Model 2023; 29:51. [PMID: 36680606 DOI: 10.1007/s00894-022-05434-y] [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: 09/17/2022] [Accepted: 12/20/2022] [Indexed: 01/22/2023]
Abstract
Molecular dynamics simulations were applied to human 5-LOX to obtain detailed information on its structure and dynamics with and without ligands. The dynamical properties evaluated based on root mean square deviations, root mean square fluctuations and secondary structure prediction helped decipher the contrast dynamic behavior of the systems pointing toward the ligand binding effect. The ligand binding to the protein also perturbed other properties of the protein such as the central bending of the protein and water coordination to the metal ion. The central bending in the protein was reported to be very significant that was associated with the allosteric modulation in the lipoxygenases; therefore, on a similar line, the central bending was evaluated in terms of hinge angle analysis which showed substantial bending between the C-terminal and the N-terminal domain via the linker residues which connects the two domains. On the other hand, the suspected water coordination to the metal ion in the protein was ruled out by computing the iron-water radial distribution function which showed that the water molecule was not found to be in the vicinity of the metal ion. Finally, the binding free energy was estimated for Zileuton and CAPE1 inhibitors bound to 5-LOX via the thermodynamic integration approach which showed that CAPE1 had a strong binding potential for the active site of the protein compared to Zileuton, and the free energy data correlated well with their IC50 values corresponding to the high inhibition potential of CAPE1 compared to Zileuton.
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Affiliation(s)
- Shahid Duran
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Syed Tarique Moin
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
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31
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Wang D, Wu Z, Shen C, Bao L, Luo H, Wang Z, Yao H, Kong DX, Luo C, Hou T. Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors. Brief Bioinform 2023; 24:6961473. [PMID: 36573494 DOI: 10.1093/bib/bbac592] [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: 09/05/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022] Open
Abstract
Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.
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Affiliation(s)
- Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Lingjie Bao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
| | - Hao Luo
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
| | - Hucheng Yao
- State Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Cheng Luo
- The Center for Chemical Biology, Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203 China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, China
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32
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Wu Q, Huang SY. HCovDock: an efficient docking method for modeling covalent protein-ligand interactions. Brief Bioinform 2023; 24:6961470. [PMID: 36573474 DOI: 10.1093/bib/bbac559] [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: 08/15/2022] [Revised: 11/02/2022] [Accepted: 11/17/2022] [Indexed: 12/28/2022] Open
Abstract
Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.
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Affiliation(s)
- Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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33
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Ma D, Li S, Chen Z. Drug-target binding affinity prediction method based on a deep graph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:269-282. [PMID: 36650765 DOI: 10.3934/mbe.2023012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The development of new drugs is a long and costly process, Computer-aided drug design reduces development costs while computationally shortening the new drug development cycle, in which DTA (Drug-Target binding Affinity) prediction is a key step to screen out potential drugs. With the development of deep learning, various types of deep learning models have achieved notable performance in a wide range of fields. Most current related studies focus on extracting the sequence features of molecules while ignoring the valuable structural information; they employ sequence data that represent only the elemental composition of molecules without considering the molecular structure maps that contain structural information. In this paper, we use graph neural networks to predict DTA based on corresponding graph data of drugs and proteins, and we achieve competitive performance on two benchmark datasets, Davis and KIBA. In particular, an MSE of 0.227 and CI of 0.895 were obtained on Davis, and an MSE of 0.127 and CI of 0.903 were obtained on KIBA.
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Affiliation(s)
- Dong Ma
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Shuang Li
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
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34
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Minadakis G, Tomazou M, Dietis N, Spyrou GM. Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. Brief Bioinform 2022; 24:6895455. [PMID: 36513376 PMCID: PMC9851336 DOI: 10.1093/bib/bbac536] [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: 07/19/2022] [Revised: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.
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Affiliation(s)
- George Minadakis
- Corresponding author: George Minadakis, Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus. Tel.: +357-22-392852; Fax: +357-22-358238; E-mail:
| | - Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
| | - Nikolas Dietis
- Medical School, University of Cyprus, Nicosia 1678, Cyprus
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus
- PO Box 23462, 1683 Nicosia, Cyprus,The Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371 Ayios Dometios, PO Box 23462, 1683 Nicosia, Cyprus
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35
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Yang X, Zheng Y, Xing X, Sui X, Jia W, Pan H. Immune subtype identification and multi-layer perceptron classifier construction for breast cancer. Front Oncol 2022; 12:943874. [PMID: 36568197 PMCID: PMC9780074 DOI: 10.3389/fonc.2022.943874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. Methods In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. Results By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. Conclusion Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.
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Affiliation(s)
- Xinbo Yang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China,*Correspondence: Yuanjie Zheng, ; Huali Pan,
| | - Xianrong Xing
- Department of Pharmacy, Shandong Medical College, Jinan, China
| | - Xiaodan Sui
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Huali Pan
- School of Information Science and Engineering, Shandong Normal University, Jinan, China,Business School, Shandong Normal University, Jinan, China,*Correspondence: Yuanjie Zheng, ; Huali Pan,
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Lipin DV, Denisova EI, Shipilovskikh DA, Makhmudov RR, Igidov NM, Shipilovskikh SA. Synthesis, Intramolecular Cyclization, and Anti-inflammatory Activity of Substituted 2-[2-(4-R-Benzoyl)hydrazinylidene]-4-oxobutanoic Acids. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1070428022120041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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37
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Qu X, Dong L, Zhang J, Si Y, Wang B. Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules. J Chem Inf Model 2022; 62:4369-4379. [PMID: 36083808 DOI: 10.1021/acs.jcim.2c00916] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Jinyan Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
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38
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Mohamed EAR, Abdelwahab SF, Alqaisi AM, Nasr AMS, Hassan HA. Identification of promising anti-EBOV inhibitors: de novo drug design, molecular docking and molecular dynamics studies. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220369. [PMID: 36177201 PMCID: PMC9515638 DOI: 10.1098/rsos.220369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/22/2022] [Indexed: 06/16/2023]
Abstract
The Ebola virus (EBOV) outbreak was recorded as the largest in history and caused many fatalities. As seen in previous studies, drug repurposing and database filtration were the two major pathways to searching for potent compounds against EBOV. In this study, a deep learning (DL) approach via the LigDream tool was employed to obtain novel and effective anti-EBOV inhibitors. Based on the galidesivir (BCX4430) chemical structure, 100 compounds were collected and inspected using various in silico approaches. Results from the molecular docking study indicated that mol1_069 and mol1_092 were the best two potent compounds with a docking score of -7.1 kcal mol-1 and -7.0 kcal mol-1, respectively. Molecular dynamics simulations, in addition to binding energy calculations, were conducted over 100 ns. Both compounds exhibited lower binding energies than BCX4430. Furthermore, compared with BCX4430 (%Absorption = 60.6%), mol1_069 and mol1_092 scored higher values of % Absorption equal to 68.1% and 63.7%, respectively. The current data point to the importance of using DL in the drug design process instead of conventional methods such as drug repurposing or database filtration. In conclusion, mol1_069 and mol1_092 are promising anti-EBOV drug candidates that require further in vitro and in vivo investigations.
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Affiliation(s)
- Eslam A. R. Mohamed
- Department of Chemistry, Faculty of Science, Minia University, Minia 61511, Egypt
| | - Sayed F. Abdelwahab
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif 21944, Saudi Arabia
| | | | | | - Heba Ali Hassan
- Department of Pharmacognosy, Faculty of Pharmacy, Sohag University, Sohag 82524, Egypt
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39
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B 2022; 126:6372-6383. [PMID: 35976160 PMCID: PMC9442638 DOI: 10.1021/acs.jpcb.2c04346] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 02/08/2023]
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
- Department
of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer
Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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Lalaurie CJ, Splevins A, Barata TS, Bunting KA, Higazi DR, Zloh M, Spiteri VA, Perkins SJ, Dalby PA. Elucidation of critical pH-dependent structural changes in Botulinum Neurotoxin E. J Struct Biol 2022; 214:107876. [PMID: 35738335 DOI: 10.1016/j.jsb.2022.107876] [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: 03/22/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 10/18/2022]
Abstract
Botulinum Neurotoxins (BoNT) are the most potent toxins currently known. However, they also have therapeutic applications for an increasing number of motor related conditions due to their specificity, and low diffusion into the system. Although the start- and end- points for the BoNT mechanism of action are well-studied, a critical step remains poorly understood. It is theorised that BoNTs undergo a pH-triggered conformational shift, activating the neurotoxin by priming it to form a transmembrane (TM) channel. To test this hypothesis, we combined molecular dynamics (MD) simulations and small-angle x-ray scattering (SAXS), revealing a new conformation of serotype E (BoNT/E). This conformation was exclusively observed in simulations below pH 5.5, as determined by principal component analysis (PCA), and its theoretical SAXS profile matched an experimental SAXS profile obtained at pH 4. Additionally, a localised secondary structural change was observed in MD simulations below pH 5.5, in a region previously identified as instrumental for membrane insertion for serotype A (BoNT/A). These changes were found at a critical pH value for BoNTs in vivo, and may be relevant for their therapeutic use.
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Affiliation(s)
- Christophe J Lalaurie
- Department of Biochemical Engineering, Bernard Katz Building, University College London, Gordon Street, London WC1H 0AH, UK
| | - Andrew Splevins
- Evox Therapeutics Ltd, Oxford Science Park, Medwar Center, Oxford, England OX4 4HG, UK; Ipsen Bioinnovation, 102 Park Drive, Milton Park, Abingdon, Oxfordshire OX14 4RY UK
| | - Teresa S Barata
- FairJourney Biologics, 823 Rua do Campo Alegre, Porto, Porto 4150-180, Portugal; Ipsen Bioinnovation, 102 Park Drive, Milton Park, Abingdon, Oxfordshire OX14 4RY UK
| | - Karen A Bunting
- Ipsen Bioinnovation, 102 Park Drive, Milton Park, Abingdon, Oxfordshire OX14 4RY UK
| | - Daniel R Higazi
- Ipsen Biopharm Ltd., Wrexham Industrial Estate, 9 Ash Road, LL13 9UF, UK
| | - Mire Zloh
- UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Valentina A Spiteri
- Department of Structural and Molecular Biology, Division of Biosciences, Darwin Building, University College London, Gower Street, London WC1E 6BT, UK
| | - Stephen J Perkins
- Department of Structural and Molecular Biology, Division of Biosciences, Darwin Building, University College London, Gower Street, London WC1E 6BT, UK
| | - Paul A Dalby
- Department of Biochemical Engineering, Bernard Katz Building, University College London, Gordon Street, London WC1H 0AH, UK.
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41
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Zhu S, Bai Q, Li L, Xu T. Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents. Comput Struct Biotechnol J 2022; 20:2839-2847. [PMID: 35765655 PMCID: PMC9189996 DOI: 10.1016/j.csbj.2022.05.057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 12/19/2022] Open
Abstract
Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.
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Affiliation(s)
- Sha Zhu
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
| | - Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
- Corresponding author.
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