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Kamble P, Nagar PR, Bhakhar KA, Garg P, Sobhia ME, Naidu S, Bharatam PV. Cancer pharmacoinformatics: Databases and analytical tools. Funct Integr Genomics 2024; 24:166. [PMID: 39294509 DOI: 10.1007/s10142-024-01445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/26/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.
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Affiliation(s)
- Pradnya Kamble
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prinsa R Nagar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Kaushikkumar A Bhakhar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - M Elizabeth Sobhia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Srivatsava Naidu
- Center of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Prasad V Bharatam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
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2
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Park S, Silva E, Singhal A, Kelly MR, Licon K, Panagiotou I, Fogg C, Fong S, Lee JJY, Zhao X, Bachelder R, Parker BA, Yeung KT, Ideker T. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. NATURE CANCER 2024; 5:996-1009. [PMID: 38443662 PMCID: PMC11286358 DOI: 10.1038/s43018-024-00740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.
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Affiliation(s)
- Sungjoon Park
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Erica Silva
- Program in Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Marcus R Kelly
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kate Licon
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Isabella Panagiotou
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Catalina Fogg
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Samson Fong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - John J Y Lee
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiaoyu Zhao
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Robin Bachelder
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Barbara A Parker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kay T Yeung
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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3
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Abbasi M, Carvalho FG, Ribeiro B, Arrais JP. Predicting drug activity against cancer through genomic profiles and SMILES. Artif Intell Med 2024; 150:102820. [PMID: 38553160 DOI: 10.1016/j.artmed.2024.102820] [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: 12/05/2022] [Revised: 01/09/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
Due to the constant increase in cancer rates, the disease has become a leading cause of death worldwide, enhancing the need for its detection and treatment. In the era of personalized medicine, the main goal is to incorporate individual variability in order to choose more precisely which therapy and prevention strategies suit each person. However, predicting the sensitivity of tumors to anticancer treatments remains a challenge. In this work, we propose two deep neural network models to predict the impact of anticancer drugs in tumors through the half-maximal inhibitory concentration (IC50). These models join biological and chemical data to apprehend relevant features of the genetic profile and the drug compounds, respectively. In order to predict the drug response in cancer cell lines, this study employed different DL methods, resorting to Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). In the first stage, two autoencoders were pre-trained with high-dimensional gene expression and mutation data of tumors. Afterward, this genetic background is transferred to the prediction models that return the IC50 value that portrays the potency of a substance in inhibiting a cancer cell line. When comparing RSEM Expected counts and TPM as methods for displaying gene expression data, RSEM has been shown to perform better in deep models and CNNs model can obtain better insight in these types of data. Moreover, the obtained results reflect the effectiveness of the extracted deep representations in the prediction of the IC50 value that portrays the potency of a substance in inhibiting a tumor, achieving a performance of a mean squared error of 1.06 and surpassing previous state-of-the-art models.
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Affiliation(s)
- Maryam Abbasi
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal; Polytechnic Institute of Coimbra, Applied Research Institute, Coimbra, Portugal; Research Centre for Natural Resources Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Coimbra, Portugal.
| | - Filipa G Carvalho
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Bernardete Ribeiro
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Joel P Arrais
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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Zhao X, Singhal A, Park S, Kong J, Bachelder R, Ideker T. Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress. Cancer Discov 2024; 14:508-523. [PMID: 38236062 PMCID: PMC10905674 DOI: 10.1158/2159-8290.cd-23-0641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/25/2023] [Accepted: 12/21/2023] [Indexed: 01/19/2024]
Abstract
Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine. SIGNIFICANCE Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response. This article is featured in Selected Articles from This Issue, p. 384.
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Affiliation(s)
- Xiaoyu Zhao
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
| | - Sungjoon Park
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - JungHo Kong
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
| | - Robin Bachelder
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
- Department of Bioengineering, University of California, San Diego, La Jolla, California
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Vasanthakumari P, Zhu Y, Brettin T, Partin A, Shukla M, Xia F, Narykov O, Weil MR, Stevens RL. A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening. Cancers (Basel) 2024; 16:530. [PMID: 38339281 PMCID: PMC10854925 DOI: 10.3390/cancers16030530] [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: 11/28/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.
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Affiliation(s)
- Priyanka Vasanthakumari
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (T.B.); (R.L.S.)
| | - Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Maulik Shukla
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Fangfang Xia
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Michael Ryan Weil
- Cancer Research Technology Program, Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA;
| | - Rick L. Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (T.B.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
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Narykov O, Zhu Y, Brettin T, Evrard YA, Partin A, Shukla M, Xia F, Clyde A, Vasanthakumari P, Doroshow JH, Stevens RL. Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models. Cancers (Basel) 2023; 16:50. [PMID: 38201477 PMCID: PMC10777918 DOI: 10.3390/cancers16010050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024] Open
Abstract
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.
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Affiliation(s)
- Oleksandr Narykov
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Yvonne A. Evrard
- Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA;
| | - Alexander Partin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Maulik Shukla
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Fangfang Xia
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - Austin Clyde
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
| | - Priyanka Vasanthakumari
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
| | - James H. Doroshow
- Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD 20892, USA;
| | - Rick L. Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (T.B.); (A.P.); (M.S.); (F.X.); (P.V.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
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Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 2023; 10:1086097. [PMID: 36873878 PMCID: PMC9975164 DOI: 10.3389/fmed.2023.1086097] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas S. Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Jamie Overbeek
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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Baressi Šegota S, Lorencin I, Kovač Z, Car Z. On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks. Biomedicines 2023; 11:biomedicines11020284. [PMID: 36830823 PMCID: PMC9952997 DOI: 10.3390/biomedicines11020284] [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: 12/19/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023] Open
Abstract
In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested-modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.
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Affiliation(s)
- Sandi Baressi Šegota
- Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
- Correspondence: ; Tel.: +385-51-505-715
| | - Ivan Lorencin
- Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
| | - Zoran Kovač
- Faculty of Dental Medicine, University of Rijeka, Krešimirova 40/42, 51000 Rijeka, Croatia
| | - Zlatan Car
- Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
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9
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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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10
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Singha M, Pu L, Stanfield BA, Uche IK, Rider PJF, Kousoulas KG, Ramanujam J, Brylinski M. Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors. BMC Cancer 2022; 22:1211. [PMID: 36434556 PMCID: PMC9694576 DOI: 10.1186/s12885-022-10293-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 11/07/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching. METHODS CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion. RESULTS Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03. CONCLUSIONS CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.
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Affiliation(s)
- Manali Singha
- grid.64337.350000 0001 0662 7451Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Limeng Pu
- grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Brent A. Stanfield
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Ifeanyi K. Uche
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.279863.10000 0000 8954 1233School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA 70112 USA
| | - Paul J. F. Rider
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Konstantin G. Kousoulas
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - J. Ramanujam
- grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Michal Brylinski
- grid.64337.350000 0001 0662 7451Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA
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Samal BR, Loers JU, Vermeirssen V, De Preter K. Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells. FRONTIERS IN BIOINFORMATICS 2022; 2:1036963. [PMID: 36466148 PMCID: PMC9714662 DOI: 10.3389/fbinf.2022.1036963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/03/2022] [Indexed: 01/02/2024] Open
Abstract
In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.
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Affiliation(s)
- Bikash Ranjan Samal
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Jens Uwe Loers
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
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Tong Z, Chen X, Tong S, Yang Q. Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis. ACS OMEGA 2022; 7:11422-11429. [PMID: 35415332 PMCID: PMC8992261 DOI: 10.1021/acsomega.2c00615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/10/2022] [Indexed: 05/14/2023]
Abstract
Flexible operation of large-scale boilers for electricity generation is essential in modern power systems. An accurate prediction of boiler steam temperature is of great importance to the operational efficiency of boiler units to prevent the occurrence of overtemperature. In this study, a dense, residual long short-term memory network (LSTM)-attention model is proposed for steam temperature prediction. In particular, the residual elements in the proposed model have a great advantage in improving the accuracy by adding short skip connections between layers. To provide overall information for the steam temperature prediction, uncertainty analysis based on the proposed model is performed to quantify the uncertainties in steam temperature variations. Our results demonstrate that the proposed method exhibits great performance in steam temperature prediction with a mean absolute error (MAE) of less than 0.6 °C. Compared to algorithms such as support-vector regression (SVR), ridge regression (RIDGE), the recurrent neural network (RNN), the gated recurrent unit (GRU), and LSTM, the prediction accuracy of the proposed model outperforms by 32, 16, 12, 10, and 11% in terms of MAE, respectively. According to our analysis, the dense residual LSTM-attention model is shown to provide an accurate early warning of overtemperature, enabling the development of real-time steam temperature control.
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