1
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Yang S, Zhang W, Yang B, Feng X, Li Y, Li X, Liu Q. Metagenomic evidence for antibiotic-associated actinomycetes in the Karamay Gobi region. Front Microbiol 2024; 15:1330880. [PMID: 38505550 PMCID: PMC10949947 DOI: 10.3389/fmicb.2024.1330880] [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: 10/31/2023] [Accepted: 02/07/2024] [Indexed: 03/21/2024] Open
Abstract
Due to the misuse of antibiotics, there is an increasing emergence and spread of multidrug-resistant (MDR) bacteria, leading to a human health crisis. To address clinical antibiotic resistance and prevent/control pathogenic microorganisms, the development of novel antibiotics is essential. This also offers a new approach to discovering valuable actinobacterial flora capable of producing natural bioactive products. In this study, we employed bioinformatics and macro-genome sequencing to collect 15 soil samples from three different locations in the Karamay Gobi region. First, we assessed the diversity of microorganisms in soil samples from different locations, analyzing the content of bacteria, archaea, actinomycetes, and fungi. The biodiversity of soil samples from outside the Gobi was found to be higher than that of soil samples from within and in the center of the Gobi. Second, through microbial interaction network analysis, we identified actinomycetes as the dominant group in the system. We have identified the top four antibiotic genes, such as Ecol_fabG_TRC, Efac_liaR_DAP, tetA (58), and macB, by CARD. These genes are associated with peptide antibiotics, disinfecting agents and antiseptics, tetracycline antibiotics, and macrolide antibiotics. In addition, we also obtained 40 other antibiotic-related genes through CARD alignment. Through in-depth analysis of desert soil samples, we identified several unstudied microbial species belonging to different families, including Erythrobacteriaceae, Solirubrobacterales, Thermoleophilaceae, Gaiellaceae, Nocardioidaceae, Actinomycetia, Egibacteraceae, and Acidimicrobiales. These species have the capability to produce peptide antibiotics, macrolide antibiotics, and tetracycline antibiotics, as well as disinfectants and preservatives. This study provides valuable theoretical support for future in-depth research.
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Affiliation(s)
- Shuai Yang
- Xinjiang Second Medical College, Xinjiang, China
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
| | - Wei Zhang
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
| | - Bo Yang
- Xinjiang Second Medical College, Xinjiang, China
| | - Xin Feng
- Xinjiang Second Medical College, Xinjiang, China
| | - Yiyang Li
- Xinjiang Second Medical College, Xinjiang, China
| | - Xiaolin Li
- Xinjiang Second Medical College, Xinjiang, China
| | - Qin Liu
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
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2
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Yan X, Gu C, Feng Y, Han J. Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism. Methods 2024; 223:16-25. [PMID: 38262485 DOI: 10.1016/j.ymeth.2024.01.009] [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: 10/26/2023] [Revised: 01/04/2024] [Accepted: 01/19/2024] [Indexed: 01/25/2024] Open
Abstract
Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.
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Affiliation(s)
- Xiaoying Yan
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China.
| | - Chi Gu
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
| | - Yuehua Feng
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
| | - Jiaxin Han
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
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3
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Zhang P, Zhang D, Zhou W, Wang L, Wang B, Zhang T, Li S. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform 2023; 25:bbad518. [PMID: 38197310 PMCID: PMC10777171 DOI: 10.1093/bib/bbad518] [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: 09/02/2023] [Revised: 11/03/2023] [Accepted: 11/30/2023] [Indexed: 01/11/2024] Open
Abstract
Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Dingfan Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wuai Zhou
- China Mobile Information System Integration Co., Ltd, Beijing 100032, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Boyang Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingyu Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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4
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Aspromonte MC, Conte AD, Zhu S, Tan W, Shen Y, Zhang Y, Li Q, Wang MH, Babbi G, Bovo S, Martelli PL, Casadio R, Althagafi A, Toonsi S, Kulmanov M, Hoehndorf R, Katsonis P, Williams A, Lichtarge O, Xian S, Surento W, Pejaver V, Mooney SD, Sunderam U, Srinivasan R, Murgia A, Piovesan D, Tosatto SCE, Leonardi E. CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs). RESEARCH SQUARE 2023:rs.3.rs-3209168. [PMID: 37577579 PMCID: PMC10418555 DOI: 10.21203/rs.3.rs-3209168/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.
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Affiliation(s)
| | | | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | - Wuwei Tan
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
| | | | - Qi Li
- CUHK Shenzhen Research Institute, Shenzhen
| | | | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Samuele Bovo
- Department of Agricultural and Food Sciences, University of Bologna
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Sumyyah Toonsi
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Maxat Kulmanov
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030
| | - Su Xian
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Wesley Surento
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195
| | - Uma Sunderam
- Innovation Labs, Tata Consultancy Services, Hyderabad
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5
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Leng D, Zheng L, Wen Y, Zhang Y, Wu L, Wang J, Wang M, Zhang Z, He S, Bo X. A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol 2022; 23:171. [PMID: 35945544 PMCID: PMC9361561 DOI: 10.1186/s13059-022-02739-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .
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Affiliation(s)
- Dongjin Leng
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China
| | - Linyi Zheng
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Yuqi Wen
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China
| | - Yunhao Zhang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Meihong Wang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen, People's Republic of China.
| | - Song He
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China.
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, People's Republic of China.
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6
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Feng YH, Zhang SW. Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs. Molecules 2022; 27:molecules27093004. [PMID: 35566354 PMCID: PMC9105425 DOI: 10.3390/molecules27093004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022] Open
Abstract
The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.
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7
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Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00469-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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8
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Cantrell JM, Chung CH, Chandrasekaran S. Machine learning to design antimicrobial combination therapies: promises and pitfalls. Drug Discov Today 2022; 27:1639-1651. [DOI: 10.1016/j.drudis.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/20/2022] [Accepted: 04/04/2022] [Indexed: 01/13/2023]
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9
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Creanza TM, Lamanna G, Delre P, Contino M, Corriero N, Saviano M, Mangiatordi GF, Ancona N. DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues. J Chem Inf Model 2022; 62:1411-1424. [PMID: 35294184 DOI: 10.1021/acs.jcim.2c00205] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.
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Affiliation(s)
- Teresa Maria Creanza
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
| | - Giuseppe Lamanna
- Chemistry Department, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy.,CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Pietro Delre
- Chemistry Department, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy.,CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Marialessandra Contino
- Department of Pharmacy─Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125 Bari, Italy
| | - Nicola Corriero
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | - Michele Saviano
- CNR─Institute of Crystallography, Via Amendola 122/o, 70126 Bari, Italy
| | | | - Nicola Ancona
- CNR─Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Amendola 122/o, 70126 Bari, Italy
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10
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Sousa T, Correia J, Pereira V, Rocha M. Generative Deep Learning for Targeted Compound Design. J Chem Inf Model 2021; 61:5343-5361. [PMID: 34699719 DOI: 10.1021/acs.jcim.0c01496] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.
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Affiliation(s)
- Tiago Sousa
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - João Correia
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Campus Gualtar, University of Minho, 4710-057 Braga, Portugal
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11
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Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Brief Bioinform 2021; 22:bbab293. [PMID: 34368832 PMCID: PMC8574999 DOI: 10.1093/bib/bbab293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/08/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.
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Affiliation(s)
- Betül Güvenç Paltun
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- University of Manchester, UK
| | - Hiroshi Mamitsuka
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 6110011, Japan
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12
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Guo WF, Zhang SW, Feng YH, Liang J, Zeng T, Chen L. Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients. Nucleic Acids Res 2021; 49:e37. [PMID: 33434272 PMCID: PMC8053130 DOI: 10.1093/nar/gkaa1272] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/02/2020] [Accepted: 12/22/2020] [Indexed: 12/27/2022] Open
Abstract
Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.
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Affiliation(s)
- Wei-Feng Guo
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China.,School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China
| | - Yue-Hua Feng
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian 710072, China
| | - Jing Liang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Tao Zeng
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy Science, Shanghai 200031, China
| | - Luonan Chen
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy Science, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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13
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Yang S, Zhu F, Ling X, Liu Q, Zhao P. Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Front Genet 2021; 12:607471. [PMID: 33912213 PMCID: PMC8075004 DOI: 10.3389/fgene.2021.607471] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/05/2021] [Indexed: 12/24/2022] Open
Abstract
With the progress of medical technology, biomedical field ushered in the era of big data, based on which and driven by artificial intelligence technology, computational medicine has emerged. People need to extract the effective information contained in these big biomedical data to promote the development of precision medicine. Traditionally, the machine learning methods are used to dig out biomedical data to find the features from data, which generally rely on feature engineering and domain knowledge of experts, requiring tremendous time and human resources. Different from traditional approaches, deep learning, as a cutting-edge machine learning branch, can automatically learn complex and robust feature from raw data without the need for feature engineering. The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full use of biomedical data and improving medical health level. Deep learning plays an increasingly important role in the field of medical health and has a broad prospect of application. However, the problems and challenges of deep learning in computational medical health still exist, including insufficient data, interpretability, data privacy, and heterogeneity. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.
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Affiliation(s)
- Sijie Yang
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Xinghong Ling
- School of Computer Science and Technology, Soochow University, Suzhou, China
- WenZheng College of Soochow University, Suzhou, China
| | - Quan Liu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Peiyao Zhao
- School of Computer Science and Technology, Soochow University, Suzhou, China
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