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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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2
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Ding Y, Xue X. Medicinal Chemistry Strategies for the Modification of Bioactive Natural Products. Molecules 2024; 29:689. [PMID: 38338433 PMCID: PMC10856770 DOI: 10.3390/molecules29030689] [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: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
Natural bioactive compounds are valuable resources for drug discovery due to their diverse and unique structures. However, these compounds often lack optimal drug-like properties. Therefore, structural optimization is a crucial step in the drug development process. By employing medicinal chemistry principles, targeted molecular operations can be applied to natural products while considering their size and complexity. Various strategies, including structural fragmentation, elimination of redundant atoms or groups, and exploration of structure-activity relationships, are utilized. Furthermore, improvements in physicochemical properties, chemical and metabolic stability, biophysical properties, and pharmacokinetic properties are sought after. This article provides a concise analysis of the process of modifying a few marketed drugs as illustrative examples.
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Affiliation(s)
- Yuyang Ding
- Shenzhen Borui Pharmaceutical Technology Co., Ltd., Shenzhen 518055, China;
| | - Xiaoqian Xue
- Medi-X Pingshan, Southern University of Science and Technology, Shenzhen 518055, China
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3
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Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, Porwal O, Fuloria NK. Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer. Curr Drug Deliv 2024; 21:870-886. [PMID: 37670704 DOI: 10.2174/1567201821666230905090621] [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: 10/31/2022] [Revised: 03/08/2023] [Accepted: 03/24/2023] [Indexed: 09/07/2023]
Abstract
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
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Affiliation(s)
- Vishal Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Amit Singh
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Sanjana Chauhan
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Pramod Kumar Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Shubham Chaudhary
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Astha Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Omji Porwal
- Department of Pharmacognosy, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
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Pal S, Bhattacharya M, Islam MA, Chakraborty C. ChatGPT or LLM in next-generation drug discovery and development: pharmaceutical and biotechnology companies can make use of the artificial intelligence-based device for a faster way of drug discovery and development. Int J Surg 2023; 109:4382-4384. [PMID: 37707542 PMCID: PMC10720782 DOI: 10.1097/js9.0000000000000719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 08/20/2023] [Indexed: 09/15/2023]
Affiliation(s)
- Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu
| | | | - Md. Aminul Islam
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, India
- Advanced Molecular Lab, Department of Microbiology, President Abdul Hamid Medical College, Karimganj, Kishoreganj
| | - Chiranjib Chakraborty
- Department of Microbiology, COVID-19 Diagnostic Lab, Noakhali Science and Technology University, Noakhali, Bangladesh
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Li B, Lin M, Chen T, Wang L. FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction. Brief Bioinform 2023; 24:bbad398. [PMID: 37930026 DOI: 10.1093/bib/bbad398] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/25/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials. In this study, we propose a self-supervised pretraining deep learning (DL) framework, called functional group bidirectional encoder representations from transformers (FG-BERT), pertained based on ~1.45 million unlabeled drug-like molecules, to learn meaningful representation of molecules from function groups. The pretrained FG-BERT framework can be fine-tuned to predict molecular properties. Compared to state-of-the-art (SOTA) machine learning and DL methods, we demonstrate the high performance of FG-BERT in evaluating molecular properties in tasks involving physical chemistry, biophysics and physiology across 44 benchmark datasets. In addition, FG-BERT utilizes attention mechanisms to focus on FG features that are critical to the target properties, thereby providing excellent interpretability for downstream training tasks. Collectively, FG-BERT does not require any artificially crafted features as input and has excellent interpretability, providing an out-of-the-box framework for developing SOTA models for a variety of molecule (especially for drug) discovery tasks.
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Affiliation(s)
- Biaoshun Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Mujie Lin
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Tiegen Chen
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Room 109, Building C, SSIP Healthcare and Medicine Demonstration Zone, Zhongshan Tsuihang New District, Zhongshan, Guangdong, 528400, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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6
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Oliveira APS, Lima DR, Bezerra LL, Monteiro NKV, Loiola OD, Silva MGV. Virtual screening of flavonoids from Chamaecrista genus: ADME and pharmacokinetic properties, interactions of flavonoid-DNA complex by molecular docking and molecular dynamics. J Biomol Struct Dyn 2023; 41:7677-7685. [PMID: 36120963 DOI: 10.1080/07391102.2022.2124455] [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: 05/09/2022] [Accepted: 09/05/2022] [Indexed: 10/14/2022]
Abstract
This research aimed to conduct an in silico study of compounds, mainly flavonoids, that are found in several plants, including the species of the Chamaecrista genus. The ADME properties, the drug-likeness score and properties of Lipinski and Veber rules of the molecules were determined using online databases. Based on the predicted properties, four flavonoids, apigenin, fisetin, luteolin and ononin were selected for molecular docking and dynamic simulations to study their interactions with DNA (PDB ID: 1BNA). The molecular docking showed that ononin has a high affinity for B-DNA, exhibiting a ΔG value of -9.3 kcal mol-1, compared with the other flavonoids. The molecular dynamic simulations of the flavonoid-DNA complexes showed that the flavonoids interacted with DNA by hydrogen bonding, hydrophobic interaction and π-stacking. The flavonoid ononin showed the best interaction energy value of -291.3490 kJ mol-1, compared with the other flavonoids.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ana Paula S Oliveira
- Department of Organic and Inorganic Chemistry, Federal University of Ceará, Fortaleza, Brazil
| | - Daniele R Lima
- Department of Physico-chemical and Analytic Chemistry, Federal University of Ceará, Fortaleza, Brazil
| | - Lucas L Bezerra
- Department of Physico-chemical and Analytic Chemistry, Federal University of Ceará, Fortaleza, Brazil
| | - Norberto K V Monteiro
- Department of Physico-chemical and Analytic Chemistry, Federal University of Ceará, Fortaleza, Brazil
| | - Otília D Loiola
- Department of Organic and Inorganic Chemistry, Federal University of Ceará, Fortaleza, Brazil
| | - Maria Goretti V Silva
- Department of Organic and Inorganic Chemistry, Federal University of Ceará, Fortaleza, Brazil
- Department of Physico-chemical and Analytic Chemistry, Federal University of Ceará, Fortaleza, Brazil
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7
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Kate A, Seth E, Singh A, Chakole CM, Chauhan MK, Singh RK, Maddalwar S, Mishra M. Artificial Intelligence for Computer-Aided Drug Discovery. Drug Res (Stuttg) 2023; 73:369-377. [PMID: 37276884 DOI: 10.1055/a-2076-3359] [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: 06/07/2023]
Abstract
The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.
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Affiliation(s)
- Aditya Kate
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Ekkita Seth
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Ananya Singh
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
| | - Chandrashekhar Mahadeo Chakole
- Bajiraoji Karanjekar college of Pharmacy, Sakoli, Dist-Bhandara, India
- NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi
| | - Meenakshi Kanwar Chauhan
- NDDS Research Lab, Delhi Institute of Pharmaceutical Sciences and Research, DPSR-University, New Delhi
| | - Ravi Kant Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | | | - Mohit Mishra
- Amity Institute of Biotechnology, Amity University, Chhattisgarh, India
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8
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Wu X, Li Z, Chen G, Yin Y, Chen CYC. Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease. Front Mol Biosci 2023; 10:1227371. [PMID: 37441162 PMCID: PMC10334190 DOI: 10.3389/fmolb.2023.1227371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug-target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein-protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA.
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Affiliation(s)
- Xialin Wu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhuojian Li
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Yiyang Yin
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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9
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Krentzel D, Shorte SL, Zimmer C. Deep learning in image-based phenotypic drug discovery. Trends Cell Biol 2023:S0962-8924(22)00262-8. [PMID: 36623998 DOI: 10.1016/j.tcb.2022.11.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/26/2022] [Accepted: 11/29/2022] [Indexed: 01/08/2023]
Abstract
Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).
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Affiliation(s)
- Daniel Krentzel
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France; Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France.
| | - Spencer L Shorte
- Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France; Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015 Paris, France
| | - Christophe Zimmer
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France; Institut Pasteur, Joint International Unit Artificial Intelligence for Image-based Drug Discovery & Development (PIU-Ai3D), F-75015 Paris, France.
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Cai H, Zhang H, Zhao D, Wu J, Wang L. FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction. Brief Bioinform 2022; 23:6702671. [PMID: 36124766 DOI: 10.1093/bib/bbac408] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/28/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022] Open
Abstract
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.
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Affiliation(s)
- Hanxuan Cai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Huimin Zhang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Duancheng Zhao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jingxing Wu
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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11
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Bonanni D, Pinzi L, Rastelli G. Development of machine learning classifiers to predict compound activity on prostate cancer cell lines. J Cheminform 2022; 14:77. [PMID: 36348374 PMCID: PMC9641853 DOI: 10.1186/s13321-022-00647-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/27/2022] [Indexed: 11/11/2022] Open
Abstract
Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents.
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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13
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Vandersluis S, Reid JC, Orlando L, Bhatia M. Evidence-based support for phenotypic drug discovery in acute myeloid leukemia. Drug Discov Today 2022; 27:103407. [DOI: 10.1016/j.drudis.2022.103407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/01/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2022]
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14
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Factors Determining Plasticity of Responses to Drugs. Int J Mol Sci 2022; 23:ijms23042068. [PMID: 35216184 PMCID: PMC8877660 DOI: 10.3390/ijms23042068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 12/16/2022] Open
Abstract
The plasticity of responses to drugs is an ever-present confounding factor for all aspects of pharmacology, influencing drug discovery and development, clinical use and the expectations of the patient. As an introduction to this Special Issue of the journal IJMS on pharmacological plasticity, we address the various levels at which plasticity appears and how such variability can be controlled, describing the ways in which drug responses can be affected with examples. The various levels include the molecular structures of drugs and their receptors, expression of genes for drug receptors and enzymes involved in metabolism, plasticity of cells targeted by drugs, tissues and clinical variables affected by whole body processes, changes in geography and the environment, and the influence of time and duration of changes. The article provides a rarely considered bird’s eye view of the problem and is intended to emphasize the need for increased awareness of pharmacological plasticity and to encourage further debate.
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15
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He S, Zhao D, Ling Y, Cai H, Cai Y, Zhang J, Wang L. Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells. Front Pharmacol 2022; 12:796534. [PMID: 34975493 PMCID: PMC8719637 DOI: 10.3389/fphar.2021.796534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/02/2021] [Indexed: 12/22/2022] Open
Abstract
Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.
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Affiliation(s)
- Shuyun He
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Duancheng Zhao
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yanle Ling
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hanxuan Cai
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yike Cai
- Center for Certification and Evaluation, Guangdong Drug Administration, Guangzhou, China
| | - Jiquan Zhang
- State Key Laboratory of Functions and Applications of Medicinal Plants, College of Pharmacy, Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, Guizhou Medical University, Guiyang, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.,Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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16
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Knight S, Gianni D, Hendricks A. Fragment-based screening: A new paradigm for ligand and target discovery. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2022; 27:3-7. [PMID: 35058174 DOI: 10.1016/j.slasd.2021.10.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Significant advances in fragment-based screening, including the emergence of Fully Functionalised Fragments (FFFs) and innovations in Covalent Fragment screening are providing a new paradigm for ligand and target discovery. FFFs offer some key distinct advantages over other screening modalities such as small molecules and genetic screens, including 1) An ability to access diverse chemical space employing a relatively small compound set 2) Ease of screen optimisation given there is no requirement for genetic manipulation and 3) Built-in proteomics tools to facilitate rapid target deconvolution directly in cells. Covalent fragments enable exploration of novel druggable nodes through irreversible fragment-cysteine interactions, complementing their fully functionalized counterparts. Both FFFs and Covalent fragments present the phenotypic screening community with an additional and complementary approach for disease centric target identification.
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Affiliation(s)
- Sinéad Knight
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK; Bioscience, Sygnature Discovery Ltd, Alderley Park, Cheshire, UK.
| | - Davide Gianni
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Adam Hendricks
- Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, Boston, USA; Civetta Therapeutics, 10 Wilson Road, Cambridge, MA 02140
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17
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Benchoua A, Lasbareilles M, Tournois J. Contribution of Human Pluripotent Stem Cell-Based Models to Drug Discovery for Neurological Disorders. Cells 2021; 10:cells10123290. [PMID: 34943799 PMCID: PMC8699352 DOI: 10.3390/cells10123290] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 02/07/2023] Open
Abstract
One of the major obstacles to the identification of therapeutic interventions for central nervous system disorders has been the difficulty in studying the step-by-step progression of diseases in neuronal networks that are amenable to drug screening. Recent advances in the field of human pluripotent stem cell (PSC) biology offers the capability to create patient-specific human neurons with defined clinical profiles using reprogramming technology, which provides unprecedented opportunities for both the investigation of pathogenic mechanisms of brain disorders and the discovery of novel therapeutic strategies via drug screening. Many examples not only of the creation of human pluripotent stem cells as models of monogenic neurological disorders, but also of more challenging cases of complex multifactorial disorders now exist. Here, we review the state-of-the art brain cell types obtainable from PSCs and amenable to compound-screening formats. We then provide examples illustrating how these models contribute to the definition of new molecular or functional targets for drug discovery and to the design of novel pharmacological approaches for rare genetic disorders, as well as frequent neurodegenerative diseases and psychiatric disorders.
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Affiliation(s)
- Alexandra Benchoua
- Neuroplasticity and Therapeutics, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- High Throughput Screening Platform, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- Correspondence:
| | - Marie Lasbareilles
- Neuroplasticity and Therapeutics, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- UEVE UMR 861, I-STEM, AFM, 91100 Corbeil-Essonnes, France
| | - Johana Tournois
- High Throughput Screening Platform, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
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