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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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Ishfaq M, Shah SZA, Ahmad I, Rahman Z. Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches. Mol Divers 2023:10.1007/s11030-023-10690-y. [PMID: 37418166 DOI: 10.1007/s11030-023-10690-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
The role of NLRP3 inflammasome in innate immunity is newly recognized. The NLRP3 protein is a family of nucleotide-binding and oligomerization domain-like receptors as well as a pyrin domain-containing protein. It has been shown that NLRP3 may contribute to the development and progression of a variety of diseases, such as multiple sclerosis, metabolic disorders, inflammatory bowel disease, and other auto-immune and auto-inflammatory conditions. The use of machine learning methods in pharmaceutical research has been widespread for several decades. An important objective of this study is to apply machine learning approaches for the multinomial classification of NLRP3 inhibitors. However, data imbalances can affect machine learning. Therefore, a synthetic minority oversampling technique (SMOTE) has been developed to increase the sensitivity of classifiers to minority groups. The QSAR modelling was performed using 154 molecules retrieved from the ChEMBL database (version 29). The accuracy of the multiclass classification top six models was found to fall within ranges of 0.99 to 0.86, and log loss ranges of 0.2 to 2.3, respectively. The results showed that the receiver operating characteristic curve (ROC) plot values significantly improved when tuning parameters were adjusted and imbalanced data was handled. Moreover, the results demonstrated that SMOTE offers a significant advantage in handling imbalanced datasets as well as substantial improvements in overall accuracy of machine learning models. The top models were then used to predict data from unseen datasets. In summary, these QSAR classification models exhibited robust statistical results and were interpretable, which strongly supported their use for rapid screening of NLRP3 inhibitors.
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Affiliation(s)
- Muhammad Ishfaq
- College of Computer Science, Huanggang Normal University, Huanggang, 438000, China
| | - Syed Zahid Ali Shah
- Department of Pathology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Ijaz Ahmad
- The University of Agriculture Peshawar, Peshawar, 25130, Khyber Pakhtunkhwa, Pakistan
| | - Ziaur Rahman
- College of Computer Science, Huanggang Normal University, Huanggang, 438000, China.
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Halder AK, Mitra S, Cordeiro MNDS. Designing multi-target drugs for the treatment of major depressive disorder. Expert Opin Drug Discov 2023; 18:643-658. [PMID: 37183604 DOI: 10.1080/17460441.2023.2214361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
INTRODUCTION Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD. AREAS COVERED In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the pre-clinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized based on the pharmacological targets. On the basis of these important case studies, the challenges involved in different design strategies were discussed in detail. EXPERT OPINION Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modelling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
- Dr. B.C. Roy College of Pharmacy & Allied Health Sciences, Durgapur 713206, India
| | - Soumya Mitra
- Dr. B.C. Roy College of Pharmacy & Allied Health Sciences, Durgapur 713206, India
| | - Maria Natalia D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Breijyeh Z, Karaman R. Design and Synthesis of Novel Antimicrobial Agents. Antibiotics (Basel) 2023; 12:antibiotics12030628. [PMID: 36978495 PMCID: PMC10045396 DOI: 10.3390/antibiotics12030628] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The necessity for the discovery of innovative antimicrobials to treat life-threatening diseases has increased as multidrug-resistant bacteria has spread. Due to antibiotics' availability over the counter in many nations, antibiotic resistance is linked to overuse, abuse, and misuse of these drugs. The World Health Organization (WHO) recognized 12 families of bacteria that present the greatest harm to human health, where options of antibiotic therapy are extremely limited. Therefore, this paper reviews possible new ways for the development of novel classes of antibiotics for which there is no pre-existing resistance in human bacterial pathogens. By utilizing research and technology such as nanotechnology and computational methods (such as in silico and Fragment-based drug design (FBDD)), there has been an improvement in antimicrobial actions and selectivity with target sites. Moreover, there are antibiotic alternatives, such as antimicrobial peptides, essential oils, anti-Quorum sensing agents, darobactins, vitamin B6, bacteriophages, odilorhabdins, 18β-glycyrrhetinic acid, and cannabinoids. Additionally, drug repurposing (such as with ticagrelor, mitomycin C, auranofin, pentamidine, and zidovudine) and synthesis of novel antibacterial agents (including lactones, piperidinol, sugar-based bactericides, isoxazole, carbazole, pyrimidine, and pyrazole derivatives) represent novel approaches to treating infectious diseases. Nonetheless, prodrugs (e.g., siderophores) have recently shown to be an excellent platform to design a new generation of antimicrobial agents with better efficacy against multidrug-resistant bacteria. Ultimately, to combat resistant bacteria and to stop the spread of resistant illnesses, regulations and public education regarding the use of antibiotics in hospitals and the agricultural sector should be combined with research and technological advancements.
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Affiliation(s)
- Zeinab Breijyeh
- Pharmaceutical Sciences Department, Faculty of Pharmacy, Al-Quds University, Jerusalem P.O. Box 20002, Palestine
| | - Rafik Karaman
- Pharmaceutical Sciences Department, Faculty of Pharmacy, Al-Quds University, Jerusalem P.O. Box 20002, Palestine
- Department of Sciences, University of Basilicata, Via dell'Ateneo Lucano 10, 85100 Potenza, Italy
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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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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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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Nakarin F, Boonpalit K, Kinchagawat J, Wachiraphan P, Rungrotmongkol T, Nutanong S. Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation. Molecules 2022; 27:molecules27041226. [PMID: 35209011 PMCID: PMC8878292 DOI: 10.3390/molecules27041226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 11/16/2022] Open
Abstract
A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.
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Affiliation(s)
- Fahsai Nakarin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
- Correspondence: ; Tel.: +66-33-014-444
| | - Kajjana Boonpalit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Patcharapol Wachiraphan
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Thanyada Rungrotmongkol
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
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Halder AK, Cordeiro MNDS. Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules 2021; 11:1670. [PMID: 34827668 PMCID: PMC8615736 DOI: 10.3390/biom11111670] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022] Open
Abstract
The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box-Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries-i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.
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Affiliation(s)
- Amit Kumar Halder
- LAQV-REQUIMTE/Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, India
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Kleandrova VV, Scotti MT, Speck-Planche A. Indirect-Acting Pan-Antivirals vs. Respiratory Viruses: A Fresh Perspective on Computational Multi-Target Drug Discovery. Curr Top Med Chem 2021; 21:2687-2693. [PMID: 34636311 DOI: 10.2174/1568026621666211012110819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/23/2021] [Accepted: 09/23/2021] [Indexed: 12/22/2022]
Abstract
Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [including the current pandemic agent known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and the one causing influenza A (IAV) are highly contagious and deadly. These can evade the immune system defenses while causing a hyperinflammatory response that can damage different tissues/organs. Simultaneously targeting immunomodulatory proteins is a plausible antiviral strategy since it could lead to the discovery of indirect-acting pan-antiviral (IAPA) agents for the treatment of diseases caused by respiratory viruses. In this context, computational approaches, which are an essential part of the modern drug discovery campaigns, could accelerate the identification of multi-target immunomodulators. This perspective discusses the usefulness of computational multi-target drug discovery for the virtual screening (drug repurposing) of IAPA agents capable of boosting the immune system through the activation of the toll-like receptor 7 (TLR7) and/or the stimulator of interferon genes (STING) while inhibiting key pro-inflammatory proteins, such as caspase-1 and tumor necrosis factor-alpha (TNF-α).
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Affiliation(s)
- 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
| | - Marcus T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900, João Pessoa. Brazil
| | - Alejandro Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900, João Pessoa. Brazil
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Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors. Antibiotics (Basel) 2021; 10:antibiotics10081005. [PMID: 34439055 PMCID: PMC8388932 DOI: 10.3390/antibiotics10081005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.
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Toropova AP, Toropov AA. Can the Monte Carlo method predict the toxicity of binary mixtures? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39493-39500. [PMID: 33755888 DOI: 10.1007/s11356-021-13460-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R2=0.95, and RMSE=0.375.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
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Kleandrova VV, Speck-Planche A. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling. Mini Rev Med Chem 2021; 20:1357-1374. [PMID: 32013845 DOI: 10.2174/1389557520666200204123156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
Abstract
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
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Affiliation(s)
- 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
| | - Alejandro Speck-Planche
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation
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15
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Martin EJ, Zhu XW. Collaborative Profile-QSAR: A Natural Platform for Building Collaborative Models among Competing Companies. J Chem Inf Model 2021; 61:1603-1616. [PMID: 33844519 DOI: 10.1021/acs.jcim.0c01342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Massively multitask bioactivity models that transfer learning between thousands of assays have been shown to work dramatically better than separate models trained on each individual assay. In particular, the applicability domain for a given model can expand from compounds similar to those tested in that specific assay to those tested across the full complement of contributing assays. If many large companies would share their assay data and train models on the superset, predictions should be better than what each company can do alone. However, a company's compounds, targets, and activities are among their most guarded trade secrets. Strategies have been proposed to share just the individual collaborators' models, without exposing any of the training data. Profile-QSAR (pQSAR) is a two-level, multitask, stacked model. It uses profiles of level-1 predictions from single-task models for thousands of assays as compound descriptors for level-2 models. This work describes its simple and natural adaptation to safe collaboration by model sharing. Broad model sharing has not yet been implemented across multiple large companies, so there are numerous unanswered questions. Novartis was formed from several mergers and acquisitions. In principle, this should allow an internal simulation of model sharing. In practice, the lack of metadata about the origins of compounds and assays made this difficult. Nevertheless, we have attempted to simulate this process and propose some findings: multitask pQSAR is always an improvement over single-task models; collaborative multitask modeling did not improve predictions on internal compounds; collaboration did improve predictions for external compounds but far less than the purely internal multitask modeling for internal compounds; collaborative models for external compounds increasingly improve as overlap between compound collections increases; combining profiles from inside and outside the company is not best, with internal predictions better using only the inside profile and external using only the outside profile, but a consensus of models using all three profiles is best on external compounds and a good compromise on internal compounds. We anticipate similar results from other model-sharing approaches. Indeed, since collaborative pQSAR through model sharing is mathematically identical to pQSAR using actual shared data, we believe our conclusions should apply to collaborative modeling by any current method even including the unlikely scenario of directly sharing all chemical structures and assay data.
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Affiliation(s)
- Eric J Martin
- Novartis Institute for Biomedical Research, 5959 Horton Street, Emeryville, California 94608-2916, United States
| | - Xiang-Wei Zhu
- Novartis Institute for Biomedical Research, 5959 Horton Street, Emeryville, California 94608-2916, United States
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16
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Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov 2021; 16:949-959. [PMID: 33779453 DOI: 10.1080/17460441.2021.1909567] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Affiliation(s)
- José Jiménez-Luna
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Francesca Grisoni
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an Der Riss, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
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17
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Tafreshi NK, Kil H, Pandya DN, Tichacek CJ, Doligalski ML, Budzevich MM, Delva NC, Langsen ML, Vallas JA, Boulware DC, Engelman RW, Gage KL, Moros EG, Wadas TJ, McLaughlin ML, Morse DL. Lipophilicity Determines Routes of Uptake and Clearance, and Toxicity of an Alpha-Particle-Emitting Peptide Receptor Radiotherapy. ACS Pharmacol Transl Sci 2021; 4:953-965. [PMID: 33860213 DOI: 10.1021/acsptsci.1c00035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Indexed: 11/30/2022]
Abstract
Lipophilicity is explored in the biodistribution (BD), pharmacokinetics (PK), radiation dosimetry (RD), and toxicity of an internally administered targeted alpha-particle therapy (TAT) under development for the treatment of metastatic melanoma. The TAT conjugate is comprised of the chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetate), conjugated to melanocortin receptor 1 specific peptidic ligand (MC1RL) using a linker moiety and chelation of the 225Ac radiometal. A set of conjugates were prepared with a range of lipophilicities (log D 7.4 values) by varying the chemical properties of the linker. Reported are the observations that higher log D 7.4 values are associated with decreased kidney uptake, decreased absorbed radiation dose, and decreased kidney toxicity of the TAT, and the inverse is observed for lower log D 7.4 values. Animals administered TATs with lower lipophilicities exhibited acute nephropathy and death, whereas animals administered the highest activity TATs with higher lipophilicities lived for the duration of the 7 month study and exhibited chronic progressive nephropathy. Changes in TAT lipophilicity were not associated with changes in liver uptake, dose, or toxicity. Significant observations include that lipophilicity correlates with kidney BD, the kidney-to-liver BD ratio, and weight loss and that blood urea nitrogen (BUN) levels correlated with kidney uptake. Furthermore, BUN was identified as having higher sensitivity and specificity of detection of kidney pathology, and the liver enzyme alkaline phosphatase (ALKP) had high sensitivity and specificity for detection of liver damage associated with the TAT. These findings suggest that tuning radiopharmaceutical lipophilicity can effectively modulate the level of kidney uptake to reduce morbidity and improve both safety and efficacy.
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Affiliation(s)
- Narges K Tafreshi
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - HyunJoo Kil
- Department of Pharmaceutical Sciences, West Virginia University, Health Sciences Center, Morgantown, West Virginia 26506, United States.,Modulation Therapeutics, Inc., Morgantown, West Virginia 26506, United States
| | - Darpan N Pandya
- Department of Radiology, University of Iowa Health Care, Iowa City, Iowa 52242, United States
| | - Christopher J Tichacek
- Departments of Radiation Oncology and Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States.,Department of Physics, University of South Florida, Tampa, Florida 33612, United States
| | - Michael L Doligalski
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - Mikalai M Budzevich
- Small Animal Imaging Laboratory and Biostatistics and Bioinformatics Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - Nella C Delva
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - Michael L Langsen
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - John A Vallas
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - David C Boulware
- Small Animal Imaging Laboratory and Biostatistics and Bioinformatics Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - Robert W Engelman
- Departments of Pediatrics, Pathology & Cell Biology and Department of Oncologic Sciences, University of South Florida, Tampa, Florida 33612, United States
| | - Kenneth L Gage
- Departments of Radiation Oncology and Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States
| | - Eduardo G Moros
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States.,Departments of Radiation Oncology and Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States.,Department of Physics, University of South Florida, Tampa, Florida 33612, United States.,Departments of Pediatrics, Pathology & Cell Biology and Department of Oncologic Sciences, University of South Florida, Tampa, Florida 33612, United States
| | - Thaddeus J Wadas
- Department of Radiology, University of Iowa Health Care, Iowa City, Iowa 52242, United States
| | - Mark L McLaughlin
- Department of Pharmaceutical Sciences, West Virginia University, Health Sciences Center, Morgantown, West Virginia 26506, United States.,Modulation Therapeutics, Inc., Morgantown, West Virginia 26506, United States
| | - David L Morse
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States.,Department of Physics, University of South Florida, Tampa, Florida 33612, United States.,Small Animal Imaging Laboratory and Biostatistics and Bioinformatics Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida 33612, United States.,Departments of Pediatrics, Pathology & Cell Biology and Department of Oncologic Sciences, University of South Florida, Tampa, Florida 33612, United States
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18
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19
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Halder AK, Dias Soeiro Cordeiro MN. Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents. Curr Med Chem 2020; 27:697-718. [DOI: 10.2174/0929867325666181031093702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/24/2018] [Accepted: 09/19/2018] [Indexed: 11/22/2022]
Abstract
Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account
for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of
chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand
urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last
decades, different in silico methods have been successfully implemented for supporting the lengthy and
expensive drug discovery process. In the current review, we discuss recent advances pertaining to in
silico analyses towards lead identification, lead modification and target identification of antileishmaniasis
and anti-trypanosomiasis agents. We describe recent applications of some important in
silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so
forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic
anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse
methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c)
recent applications and advances in the last five years; (d) experimental validations of in silico predictions;
(e) virtual screening tools; and (f) rationale or justification for the selection of these in silico
methods.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@ REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto 4169-007, Portugal
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20
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Martinez-Mayorga K, Madariaga-Mazon A, Medina-Franco JL, Maggiora G. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opin Drug Discov 2020; 15:293-306. [PMID: 31965870 DOI: 10.1080/17460441.2020.1696307] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.
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Affiliation(s)
| | | | - José L Medina-Franco
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
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21
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Speck-Planche A. Multi-Scale Modeling in Drug Discovery Against Infectious Diseases. Mini Rev Med Chem 2019; 19:1560-1563. [PMID: 31833463 DOI: 10.2174/138955751919191024110000] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 01/13/2019] [Accepted: 01/29/2019] [Indexed: 02/02/2023]
Abstract
This work discusses the idea that drug discovery, instead of being performed through a series of filtering-based stages, should be viewed as a multi-scale optimization problem. Here, the most promising multi-scale models are analyzed in terms of their applications, advantages, and limitations in the search for more potent and safer chemicals against infectious diseases. Multi-scale de novo drug design is highlighted as an emerging paradigm, able to accelerate the discovery of more effective antimicrobial agents.
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Affiliation(s)
- Alejandro Speck-Planche
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya str., 8, b. 2, 119992, Moscow, Russian Federation
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22
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Pharmaceutical analysis combined with in-silico therapeutic and toxicological profiling on zileuton and its impurities to assist in modern drug discovery. J Pharm Biomed Anal 2019; 179:112982. [PMID: 31785932 DOI: 10.1016/j.jpba.2019.112982] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/07/2019] [Accepted: 11/09/2019] [Indexed: 12/22/2022]
Abstract
The obligatory testing of drug molecules and their impurities to protect users against toxic compounds seems to provide interesting opportunities for new drug discovery. Impurities, which proved to be non-toxic, may be explored for their own therapeutic potential and thus be a part of future drug discovery. The essential role of pharmaceutical analysis can thus be extended to achieve this purpose. The present study examined these objectives by characterizing the major degradation products of zileuton (ZLT), a 5-lipoxygenase (5-LOX) inhibitor being prevalently used to treat asthma. The drug sample was exposed to forced degradation and found susceptible to hydrolysis and oxidative stress. The obtained Forced Degradation Products (FDP's) were resolved using an earlier developed and validated Ultra-High-Pressure Liquid Chromatography Photo-Diode-Array (UHPLC-PDA) protocol. ZLT, along with acid-and alkali-stressed samples, were subjected to Liquid-chromatography Mass-spectrometry Quadrupole Time-of-flight (LC/MS-QTOF) studies. Major degradation products were isolated using Preparative TLC and characterized using Q-TOF and/or Proton nuclear magnetic resonance (1HNMR) studies. The information obtained was assembled for structural conformation. Toxicity Prediction using Komputer Assisted Technology (TOPKAT) toxicity analyses indicated some FDP's as non-toxic when compared to ZLT. Hence, these non-toxic impurities may have bio-affinity and can be explored to interact with other therapeutic targets, to assist in drug discovery. The drug molecule and the characterized FDP's were subjected to 3-Dimensional Extra Precision (3D-XP)-molecular docking to explore changes in bio-affinity for the 5-LOX enzyme (PDB Id: 3V99). One FDP was found to have a higher binding affinity than the drug itself, indicating it may be a suitable antiasthmatic. The possibility of being active at other sites cannot be neglected and this is evaluated to a reasonable extent by Prediction of Activity Spectra for Substances (PASS). Besides being antiasthmatic, some FDP's were predicted antineoplastic, antiallergic and inhibitors of Complement Factor-D.
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23
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Toropova AP, Toropov AA. Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task? Curr Top Med Chem 2019; 19:2643-2657. [PMID: 31702504 DOI: 10.2174/1568026619666191105111817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/02/2019] [Accepted: 09/04/2019] [Indexed: 12/23/2022]
Abstract
Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
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24
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Abbasi K, Poso A, Ghasemi J, Amanlou M, Masoudi-Nejad A. Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery. J Chem Inf Model 2019; 59:4528-4539. [PMID: 31661955 DOI: 10.1021/acs.jcim.9b00626] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The main problem of small molecule-based drug discovery is to find a candidate molecule with increased pharmacological activity, proper ADME, and low toxicity. Recently, machine learning has driven a significant contribution to drug discovery. However, many machine learning methods, such as deep learning-based approaches, require a large amount of training data to form accurate predictions for unseen data. In lead optimization step, the amount of available biological data on small molecule compounds is low, which makes it a challenging problem to apply machine learning methods. The main goal of this study is to design a new approach to handle these situations. To this end, source assay (auxiliary assay) knowledge is utilized to learn a better model to predict the property of new compounds in the target assay. Up to now, the current approaches did not consider that source and target assays are adapted to different target groups with different compounds distribution. In this paper, we propose a new architecture by utilizing graph convolutional network and adversarial domain adaptation network to tackle this issue. To evaluate the proposed approach, we applied it to Tox21, ToxCast, SIDER, HIV, and BACE collections. The results showed the effectiveness of the proposed approach in transferring the related knowledge from source to target data set.
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Affiliation(s)
- Karim Abbasi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics , University of Tehran , Tehran 1417614411 , Iran
| | - Antti Poso
- School of Pharmacy, Faculty of Health Sciences , University of Eastern Finland , Kuopio 80100 , Finland
| | - Jahanbakhsh Ghasemi
- Chemistry Department, Faculty of Sciences , University of Tehran , Tehran 1417614418 , Iran
| | - Massoud Amanlou
- Drug Design and Development Research Center, Department of Medicinal Chemistry , Tehran University of Medical Sciences , Tehran 1416753955 , Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics , University of Tehran , Tehran 1417614411 , Iran
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25
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Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J. Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique. Anticancer Agents Med Chem 2019; 19:148-153. [PMID: 30360729 DOI: 10.2174/1871520618666181025122318] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 09/08/2017] [Accepted: 03/21/2018] [Indexed: 01/27/2023]
Abstract
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.
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Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Emilio Benfenati
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental; Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, United States
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, United States
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26
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Dutt R, Garg V, Khatri N, Madan AK. Phytochemicals in Anticancer Drug Development. Anticancer Agents Med Chem 2019; 19:172-183. [PMID: 30398123 DOI: 10.2174/1871520618666181106115802] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 12/19/2017] [Accepted: 03/21/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND In spite of major technological advances in conventional therapies, cancer continues to remain the leading cause of mortality worldwide. Phytochemicals are gradually emerging as a rich source of effective but safer agents against many life-threatening diseases. METHODS Various phytochemicals with reported anticancer activity have been simply categorized into major phytoconstituents- alkaloids, polyphenols, saponins, tannins and terpenoids. RESULTS The adverse effects associated with currently available anticancer medications may be overcome by using plant-derived compounds either alone or in combination. Exploration of plant kingdom may provide new leads for the accelerated development of new anticancer agents. CONCLUSION Although numerous potent synthetic drugs have been introduced for cancer chemotherapy, yet their serious toxicity concerns to normal cells apart from drug resistance have emerged as the major obstacles for their clinical utility over a prolonged duration of time. Current status and potential of phytochemicals and their derivatives in cancer therapy have been briefly reviewed in the present manuscript.
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Affiliation(s)
- Rohit Dutt
- Department of Pharmacy, G.D. Goenka University, Gurgaon-122103, India
| | - Vandana Garg
- Department of Pharmaceutical Sciences, M. D. University, Rohtak-124001, India
| | - Naveen Khatri
- Faculty of Pharmaceutical Sciences, Pt. B. D. Sharma University of Health Sciences Rohtak- 124001, India
| | - Anil K Madan
- Faculty of Pharmaceutical Sciences, Pt. B. D. Sharma University of Health Sciences Rohtak- 124001, India
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27
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Wang X, Lyu J, Dong L, Xu K. Multitask learning for biomedical named entity recognition with cross-sharing structure. BMC Bioinformatics 2019; 20:427. [PMID: 31419937 PMCID: PMC6697996 DOI: 10.1186/s12859-019-3000-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 07/18/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Biomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, neural network models are used to automatically learn features. Recently, multi-task learning has been applied successfully to neural network models of biomedical literature mining. For BioNER models, using multi-task learning makes use of features from multiple datasets and improves the performance of models. RESULTS In experiments, we compared our proposed model with other multi-task models and found our model outperformed the others on datasets of gene, protein, disease categories. We also tested the performance of different dataset pairs to find out the best partners of datasets. Besides, we explored and analyzed the influence of different entity types by using sub-datasets. When dataset size was reduced, our model still produced positive results. CONCLUSION We propose a novel multi-task model for BioNER with the cross-sharing structure to improve the performance of multi-task models. The cross-sharing structure in our model makes use of features from both datasets in the training procedure. Detailed analysis about best partners of datasets and influence between entity categories can provide guidance of choosing proper dataset pairs for multi-task training. Our implementation is available at https://github.com/JogleLew/bioner-cross-sharing .
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Affiliation(s)
- Xi Wang
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
| | - Jiagao Lyu
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
| | - Li Dong
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
| | - Ke Xu
- State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.
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Miyao T, Funatsu K. Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties. J Chem Inf Model 2019; 59:2626-2641. [PMID: 31058504 DOI: 10.1021/acs.jcim.9b00093] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Identification of chemical compounds having desirable properties is a central goal of screening campaigns. Iterative screening is a means of surveying a set of compounds, during which their property values are determined and used as feedback for regression models. Quantitative models that assess the relationships between chemical structures and property/activity are repeatedly updated through this type of cycle, and the efficient sampling of compounds for the subsequent test is a key factor in the early identification of target compounds. Nevertheless, methodological approaches to comparisons and to establishing the degree of extrapolation of sampled compounds, including the effects of applicability domains, are still required. In the present study, we conducted a series of virtual experiments to assess the characteristics of different iterative screening methods. Genetic algorithm-based partial least-squares regression, support vector regression, Bayesian optimization with Gaussian Process (GP), and batch-based Bayesian optimization with GP (GP_batch) were all compared, based on the analysis of one million compounds extracted from the ZINC database. Our results show that, irrespective of the diversity of the initial set of compounds, it was possible to identify a compound having the desired property value using the appropriate screening method. However, overall, the GP_batch method was found to be preferable when evaluating properties either which are difficult to predict or for which a key factor is present in the set of molecular descriptors.
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Affiliation(s)
- Tomoyuki Miyao
- Data Science Center and Graduate School of Science and Technology , Nara Institute of Science and Technology , 8916-5 Takayama-cho , Ikoma , Nara 630-0192 , Japan
| | - Kimito Funatsu
- Data Science Center and Graduate School of Science and Technology , Nara Institute of Science and Technology , 8916-5 Takayama-cho , Ikoma , Nara 630-0192 , Japan.,Department of Chemical System Engineering, School of Engineering , The University of Tokyo , 7-3-1 Hongo , Bunkyo-ku , Tokyo 113-8656 , Japan
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Ambure P, Halder AK, González Díaz H, Cordeiro MNDS. QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models. J Chem Inf Model 2019; 59:2538-2544. [PMID: 31083984 DOI: 10.1021/acs.jcim.9b00295] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.
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Affiliation(s)
- Pravin Ambure
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
| | - Amit Kumar Halder
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
| | - Humbert González Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry , University of Porto , 4169-007 Porto , Portugal
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Bellera CL, Talevi A. Quantitative structure-activity relationship models for compounds with anticonvulsant activity. Expert Opin Drug Discov 2019; 14:653-665. [PMID: 31072145 DOI: 10.1080/17460441.2019.1613368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.
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Affiliation(s)
- Carolina L Bellera
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
| | - Alan Talevi
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
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31
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Recent advances in fragment-based computational drug design: tackling simultaneous targets/biological effects. Future Med Chem 2018; 10:2021-2024. [DOI: 10.4155/fmc-2018-0213] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Lambrinidis G, Tsantili-Kakoulidou A. Challenges with multi-objective QSAR in drug discovery. Expert Opin Drug Discov 2018; 13:851-859. [DOI: 10.1080/17460441.2018.1496079] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
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Gupta N, Pandya P, Verma S. Computational Predictions for Multi-Target Drug Design. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2018. [DOI: 10.1007/7653_2018_26] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Bellera CL, Di Ianni ME, Talevi A. The application of molecular topology for ulcerative colitis drug discovery. Expert Opin Drug Discov 2017; 13:89-101. [PMID: 29088918 DOI: 10.1080/17460441.2018.1396314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.
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Affiliation(s)
- Carolina L Bellera
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Mauricio E Di Ianni
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Alan Talevi
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
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Speck-Planche A, Dias Soeiro Cordeiro MN. Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads. ACS COMBINATORIAL SCIENCE 2017; 19:501-512. [PMID: 28437091 DOI: 10.1021/acscombsci.7b00039] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski's rule of five and its variants.
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Affiliation(s)
- Alejandro Speck-Planche
- LAQV@REQUIMTE/Department
of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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Tsopelas F, Giaginis C, Tsantili-Kakoulidou A. Lipophilicity and biomimetic properties to support drug discovery. Expert Opin Drug Discov 2017. [PMID: 28644732 DOI: 10.1080/17460441.2017.1344210] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental - based estimation of ADME properties in early drug discovery stages. Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated. Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.
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Affiliation(s)
- Fotios Tsopelas
- a Laboratory of Inorganic and Analytical Chemistry, School of Chemical Engineering , National Technical University of Athens , Athens , Greece
| | - Constantinos Giaginis
- b Department of Food Science and Nutrition , School of Environment, University of the Aegean , Myrina , Lemnos , Greece
| | - Anna Tsantili-Kakoulidou
- c Department of Pharmaceutical Chemistry, Faculty of Pharmacy , National and Kapodistrian University of Athens , Athens , Greece
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Wang T, Yuan XS, Wu MB, Lin JP, Yang LR. The advancement of multidimensional QSAR for novel drug discovery - where are we headed? Expert Opin Drug Discov 2017; 12:769-784. [PMID: 28562095 DOI: 10.1080/17460441.2017.1336157] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.
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Affiliation(s)
- Tao Wang
- a School of biological science , Jining Medical University , Jining , China.,b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Xin-Song Yuan
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Mian-Bin Wu
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Jian-Ping Lin
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Li-Rong Yang
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
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Talevi A. Computational approaches for innovative antiepileptic drug discovery. Expert Opin Drug Discov 2016; 11:1001-16. [DOI: 10.1080/17460441.2016.1216965] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Halder A, Goodarzi M. Recent Advances in Multi-Task QSAR Modeling for Drug Design. PHARMACEUTICAL SCIENCES 2015. [DOI: 10.15171/ps.2015.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Talevi A. Multi-target pharmacology: possibilities and limitations of the "skeleton key approach" from a medicinal chemist perspective. Front Pharmacol 2015; 6:205. [PMID: 26441661 PMCID: PMC4585027 DOI: 10.3389/fphar.2015.00205] [Citation(s) in RCA: 196] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 09/04/2015] [Indexed: 11/23/2022] Open
Abstract
Multi-target drugs have raised considerable interest in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance issues. Prospective drug repositioning to treat comorbid conditions is an additional, overlooked application of multi-target ligands. While medicinal chemists usually rely on some version of the lock and key paradigm to design novel therapeutics, modern pharmacology recognizes that the mid- and long-term effects of a given drug on a biological system may depend not only on the specific ligand-target recognition events but also on the influence of the repeated administration of a drug on the cell gene signature. The design of multi-target agents usually imposes challenging restrictions on the topology or flexibility of the candidate drugs, which are briefly discussed in the present article. Finally, computational strategies to approach the identification of novel multi-target agents are overviewed.
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Affiliation(s)
- Alan Talevi
- Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata , La Plata, Argentina
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