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Saifi I, Bhat BA, Hamdani SS, Bhat UY, Lobato-Tapia CA, Mir MA, Dar TUH, Ganie SA. Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science. J Biomol Struct Dyn 2024; 42:6523-6541. [PMID: 37434311 DOI: 10.1080/07391102.2023.2234039] [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: 02/12/2023] [Accepted: 07/03/2023] [Indexed: 07/13/2023]
Abstract
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ifra Saifi
- Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India
| | - Basharat Ahmad Bhat
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Syed Suhail Hamdani
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Umar Yousuf Bhat
- Department of Zoology, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | | | - Mushtaq Ahmad Mir
- Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, KSA, Saudi Arabia
| | - Tanvir Ul Hasan Dar
- Department of Biotechnology, School of Biosciences and Biotechnology, BGSB University, Rajouri, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
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2
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Sengupta A, Singh SK, Kumar R. Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer. ACS OMEGA 2024; 9:18584-18592. [PMID: 38680332 PMCID: PMC11044175 DOI: 10.1021/acsomega.4c01195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 05/01/2024]
Abstract
Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.
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Affiliation(s)
- Avik Sengupta
- Department
of Biotechnology, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
| | - Saurabh Kumar Singh
- Department
of Chemistry, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
| | - Rahul Kumar
- Department
of Biotechnology, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
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3
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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4
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Karampuri A, Perugu S. A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches. FRONTIERS IN BIOINFORMATICS 2024; 3:1328262. [PMID: 38288043 PMCID: PMC10822965 DOI: 10.3389/fbinf.2023.1328262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024] Open
Abstract
Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.
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Affiliation(s)
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, India
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Iwata H, Nakai T, Koyama T, Matsumoto S, Kojima R, Okuno Y. VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search. J Chem Inf Model 2023; 63:7392-7400. [PMID: 37993764 PMCID: PMC10716893 DOI: 10.1021/acs.jcim.3c01220] [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: 08/03/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.
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Affiliation(s)
- Hiroaki Iwata
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Taichi Nakai
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Takuto Koyama
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Shigeyuki Matsumoto
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Ryosuke Kojima
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Yasushi Okuno
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
- HPC-
and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe-shi, Hyogo 650-0047, Japan
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6
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Kleandrova VV, Ds Cordeiro MN, Speck-Planche A. Current in silico methods for multi-target drug discovery in early anticancer research: the rise of the perturbation-theory machine learning approach. Future Med Chem 2023; 15:1647-1650. [PMID: 37728008 DOI: 10.4155/fmc-2023-0241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023] Open
Affiliation(s)
- Valeria V Kleandrova
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Maria Natália Ds Cordeiro
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
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Bhrdwaj A, Abdalla M, Pande A, Madhavi M, Chopra I, Soni L, Vijayakumar N, Panwar U, Khan MA, Prajapati L, Gujrati D, Belapurkar P, Albogami S, Hussain T, Selvaraj C, Nayarisseri A, Singh SK. Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma. Appl Biochem Biotechnol 2023; 195:5094-5119. [PMID: 36976507 DOI: 10.1007/s12010-023-04430-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.
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Affiliation(s)
- Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 Cultural West Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Aditi Pande
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Osmania University, Hyderabad, 500007, Telangana State, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Lovely Soni
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Natchimuthu Vijayakumar
- Department of Physics, M.Kumarasamy College of Engineering, Karur, 639113, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Mohd Aqueel Khan
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Leena Prajapati
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India
| | - Deepika Gujrati
- Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Begumpet, Hyderabad, 500016, India
| | - Pranoti Belapurkar
- Department of Biosciences, Acropolis Institute, Indore, 453771, Madhya Pradesh, India
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Tajamul Hussain
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha College of Dental and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 600077, Tamil Nadu, India
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India.
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia.
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, 91, Sector-A, Mahalakshmi Nagar, Indore, 452010, Madhya Pradesh, India.
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India.
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Raebareli Rd, Lucknow, 226014, Uttar Pradesh, India.
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8
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Speck-Planche A, Kleandrova VV. Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles. ACS OMEGA 2022; 7:32119-32130. [PMID: 36120024 PMCID: PMC9476185 DOI: 10.1021/acsomega.2c03363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.
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Affiliation(s)
- Alejandro Speck-Planche
- Grupo
de Química Computacional y Teórica (QCT-USFQ), Departamento
de Ingeniería Química, Universidad
San Francisco de Quito, Diego de Robles y vía Interoceánica, Quito 170901, Ecuador
| | - Valeria V. Kleandrova
- Laboratory
of Fundamental and Applied Research of Quality and Technology of Food
Production, Moscow State University of Food
Production, Volokolamskoe
shosse 11, 125080, Moscow, Russian Federation
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9
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Yousefi M, Rad MS, Shakibazadeh R, Ghodrati L, Kachoie MA. Simulating a heteroatomic CBN fullerene-like nanocage towards the drug delivery of fluorouracil. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2086252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mohammad Yousefi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mehdi Salehi Rad
- Department of Chemistry, Yadegar-e Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
| | | | - Leila Ghodrati
- Department of Medicinal Plants, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Mehrdad Ataie Kachoie
- Department of Medicinal Plants, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
- Medicinal Plants Processing Center, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
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10
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ZHANG BY, ZHENG YF, ZHAO J, KANG D, WANG Z, XU LJ, LIU AL, DU GH. Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation. Chin J Nat Med 2022; 20:332-351. [DOI: 10.1016/s1875-5364(22)60180-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Indexed: 11/03/2022]
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Halder AK, Moura AS, Cordeiro MNDS. Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next? Int J Mol Sci 2022; 23:ijms23094937. [PMID: 35563327 PMCID: PMC9099502 DOI: 10.3390/ijms23094937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/24/2022] [Accepted: 04/28/2022] [Indexed: 01/27/2023] Open
Abstract
Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, West Bengal, India
| | - Ana S. Moura
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
| | - Maria Natália D. S. Cordeiro
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Correspondence: ; Tel.: +35-12-2040-2502
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12
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Yadav M, Abdalla M, Madhavi M, Chopra I, Bhrdwaj A, Soni L, Shaheen U, Prajapati L, Sharma M, Sikarwar MS, Albogami S, Hussain T, Nayarisseri A, Singh SK. Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation and Pharmacokinetic modelling of Cyclooxygenase-2 (COX-2) inhibitor for the clinical treatment of Colorectal Cancer. MOLECULAR SIMULATION 2022. [DOI: 10.1080/08927022.2022.2068799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Manasi Yadav
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, PR People’s Republic of China
| | - Maddala Madhavi
- Department of Zoology, Osmania University, Hyderabad, Telangana State, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Lovely Soni
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Uzma Shaheen
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Leena Prajapati
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | - Megha Sharma
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
| | | | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif, Saudi Arabia
| | - Tajamul Hussain
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India
- Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
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13
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Devillers J, Sartor V, Doucet JP, Doucet-Panaye A, Devillers H. In silico prediction of mosquito repellents for clothing application. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:239-257. [PMID: 35532305 DOI: 10.1080/1062936x.2022.2062871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/30/2022] [Indexed: 06/14/2023]
Abstract
Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. Molecules were described by means of 20 molecular descriptors encoding physicochemical properties, topological information and structural features. A three-layer perceptron was used as statistical tool. An accuracy of 87% was obtained for both the training and test sets. Most of the wrong predictions can be explained. Avenues for increasing the performances of the model have been proposed.
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Affiliation(s)
| | - V Sartor
- Laboratoire des IMRCP, Université de Toulouse, Toulouse, France
| | - J P Doucet
- Université de Paris, ITODYS, CNRS, Paris, France
| | | | - H Devillers
- SPO, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
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14
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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors. Biomedicines 2022; 10:biomedicines10020491. [PMID: 35203699 PMCID: PMC8962338 DOI: 10.3390/biomedicines10020491] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 02/07/2023] Open
Abstract
Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.
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15
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Speck-Planche A, Kleandrova VV, Scotti MT. In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha. Biomolecules 2021; 11:biom11121832. [PMID: 34944476 PMCID: PMC8699067 DOI: 10.3390/biom11121832] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/27/2022] Open
Abstract
Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.
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Affiliation(s)
- Alejandro Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, Brazil;
- Correspondence:
| | - Valeria V. Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe shosse 11, 125080 Moscow, Russia;
| | - Marcus T. Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, Brazil;
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16
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Chen Z, Lin L, Wu C, Li C, Xu R, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021; 41:1100-1115. [PMID: 34613667 PMCID: PMC8626610 DOI: 10.1002/cac2.12215] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/10/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.
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Affiliation(s)
- Zi‐Hang Chen
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
- Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouGuangdong510080P. R. China
| | - Li Lin
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chen‐Fei Wu
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chao‐Feng Li
- Artificial Intelligence LaboratoryState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Rui‐Hua Xu
- Department of Medical OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Ying Sun
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
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17
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Das S, Amin SA, Jha T. Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors - Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies - First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:655-687. [PMID: 34355614 DOI: 10.1080/1062936x.2021.1955414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/11/2021] [Indexed: 06/13/2023]
Abstract
Gelatinases [gelatinase A - matrix metalloproteinase-2 (MMP-2), gelatinase B - matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.
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Affiliation(s)
- S Das
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S A Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - T Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, Speck-Planche A, Singh SK. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Curr Drug Targets 2021; 22:631-655. [PMID: 33397265 DOI: 10.2174/1389450122999210104205732] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022]
Abstract
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Poonam Tanwar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Garima Thakur
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. 8940577, San Joaquin, Santiago, Chile
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
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Halder AK, Dias Soeiro Cordeiro MN. QSAR-Co-X: an open source toolkit for multitarget QSAR modelling. J Cheminform 2021; 13:29. [PMID: 33858509 PMCID: PMC8048082 DOI: 10.1186/s13321-021-00508-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 03/31/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable. ![]()
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal.
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20
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Toropov AA, Toropova AP. The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law? Toxicol Lett 2021; 340:133-140. [PMID: 33484841 DOI: 10.1016/j.toxlet.2021.01.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/23/2020] [Accepted: 01/16/2021] [Indexed: 12/01/2022]
Abstract
Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R2 = 0.60; RMSE = 0.63).
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Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
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