1
|
Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [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: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
Collapse
Affiliation(s)
- Didem Rodoplu Solovchuk
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan.
| |
Collapse
|
2
|
Park GJ, Kang NS. ADis-QSAR: a machine learning model based on biological activity differences of compounds. J Comput Aided Mol Des 2023:10.1007/s10822-023-00517-1. [PMID: 37382799 DOI: 10.1007/s10822-023-00517-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/26/2023] [Indexed: 06/30/2023]
Abstract
Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for developing new drugs, and quantitative structure-activity relationship (QSAR) analysis has generally been used to perform this task. QSAR models with good predictive power improve the cost and time efficiencies invested in compound development. Generating these good models depends on how well differences between "active" and "inactive" compound groups can be conveyed to the model to be learned. Efforts to solve this difference issue have been made, including generating a "molecular descriptor" that compressively expresses the structural characteristics of compounds. From the same perspective, we succeeded in developing the Activity Differences-Quantitative Structure-Activity Relationship (ADis-QSAR) model by generating molecular descriptors that more explicitly convey features of the group through a pair system that performs direct connections between active and inactive groups. We used popular machine learning algorithms, such as Support Vector Machine, Random Forest, XGBoost and Multi-Layer Perceptron for model learning and evaluated the model using scores such as accuracy, area under curve, precision and specificity. The results showed that the Support Vector Machine performed better than the others. Notably, the ADis-QSAR model showed significant improvements in meaningful scores such as precision and specificity compared to the baseline model, even in datasets with dissimilar chemical spaces. This model reduces the risk of selecting false positive compounds, improving the efficiency of drug development.
Collapse
Affiliation(s)
- Gyoung Jin Park
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro,Yuseong-gu, Daejeon, 34134, Korea
| | - Nam Sook Kang
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro,Yuseong-gu, Daejeon, 34134, Korea.
| |
Collapse
|
3
|
Zhu T, Chen Y, Tao C. Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159448. [PMID: 36252662 DOI: 10.1016/j.scitotenv.2022.159448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/06/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log Kws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| | - Ying Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| |
Collapse
|
4
|
Zhu T, Tao C, Cheng H, Cong H. Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 846:157455. [PMID: 35863580 DOI: 10.1016/j.scitotenv.2022.157455] [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: 05/25/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.
Collapse
Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
| | - Haibing Cong
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.
| |
Collapse
|
5
|
Ahmadi S, Abdolmaleki A, Jebeli Javan M. In silico study of natural antioxidants. VITAMINS AND HORMONES 2022; 121:1-43. [PMID: 36707131 DOI: 10.1016/bs.vh.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.
Collapse
Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Azizeh Abdolmaleki
- Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
| | - Marjan Jebeli Javan
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
6
|
Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104851] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R2 ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.
Collapse
|
7
|
Schindl A, Hawker RR, Schaffarczyk McHale KS, Liu KTC, Morris DC, Hsieh AY, Gilbert A, Prescott SW, Haines RS, Croft AK, Harper JB, Jäger CM. Controlling the outcome of S N2 reactions in ionic liquids: from rational data set design to predictive linear regression models. Phys Chem Chem Phys 2020; 22:23009-23018. [PMID: 33043942 DOI: 10.1039/d0cp04224b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Rate constants for a bimolecular nucleophilic substitution (SN2) process in a range of ionic liquids are correlated with calculated parameters associated with the charge localisation on the cation of the ionic liquid (including the molecular electrostatic potential). Simple linear regression models proved effective, though the interdependency of the descriptors needs to be taken into account when considering generality. A series of ionic liquids were then prepared and evaluated as solvents for the same process; this data set was rationally chosen to incorporate homologous series (to evaluate systematic variation) and functionalities not available in the original data set. These new data were used to evaluate and refine the original models, which were expanded to include simple artificial neural networks. Along with showing the importance of an appropriate data set and the perils of overfitting, the work demonstrates that such models can be used to reliably predict ionic liquid solvent effects on an organic process, within the limits of the data set.
Collapse
Affiliation(s)
- Alexandra Schindl
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Rebecca R Hawker
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | | | - Kenny T-C Liu
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Daniel C Morris
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia. and School of Chemical Engineering, University of New South Wales, UNSW Sydney, 2052, Australia
| | - Andrew Y Hsieh
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Alyssa Gilbert
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Stuart W Prescott
- School of Chemical Engineering, University of New South Wales, UNSW Sydney, 2052, Australia
| | - Ronald S Haines
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Anna K Croft
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
| | - Jason B Harper
- School of Chemistry, University of New South Wales, UNSW Sydney, 2052, Australia.
| | - Christof M Jäger
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
| |
Collapse
|
8
|
Zhang QR, Zhong ZF, Sang W, Xiong W, Tao HX, Zhao GD, Li ZX, Ma QS, Tse AKW, Hu YJ, Yu H, Wang YT. Comparative comprehension on the anti-rheumatic Chinese herbal medicine Siegesbeckiae Herba: Combined computational predictions and experimental investigations. JOURNAL OF ETHNOPHARMACOLOGY 2019; 228:200-209. [PMID: 30240786 DOI: 10.1016/j.jep.2018.09.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 08/19/2018] [Accepted: 09/16/2018] [Indexed: 06/08/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Siegesbeckiae Herba (SH) is a traditional anti-rheumatic herbal medicine in China. The SH-derived product is the first licensed traditional herbal medicinal product for the management of rheumatism-induced joint and muscle pain in United Kingdom. The authenticated plant origins listed in the official Chinese Pharmacopeia for SH include Siegesbeckia orientalis L. (SO), S. pubescens Markino (SP) and S. glabrescens Markino (SG). Although the therapeutic effects of these SH species in treating rheumatoid arthritis (RA) are similar, their difference in chemical profiles suggested their anti-rheumatisms mechanisms and effects may be different. AIM OF THE STUDY This study was designed to comparatively comprehend the chemical and biological similarity and difference of SO, SP and SG for treating rheumatoid arthritis based on the combination of computational predictions and biological experiment investigations. MATERIALS AND METHODS The reported compounds for SO, SP and SG were obtained from four chemical databases (SciFinder, Combined Chemical Dictionary v2009, Dictionary of Natural Products and Chinese academy of sciences Chemistry Database). The RA-relevant proteins involved in nuclear factor-kappa B (NF-κB), oxidative stress and autophagy signaling pathways were collected from the databases of Kyoto Encyclopedia of Genes and Genomes and Biocarta. The comparative comprehension of SH plants was performed using similarity analysis, molecular docking and compounds-protein network analysis. The chemical characterization of different SH extracts were qualitatively and quantitatively analyzed, and their effects on specific RA-relevant protein expressions were investigated using Western blotting analysis. RESULTS Chemical analysis revealed that SO contains mainly sequiterpenes and pimarenoids; SP contains mainly pimarenoids, sequiterpenes, and kaurenoids; and SG contains mainly pimarenoids, flavonoids and alkaloids. Moreover, coincided with the predicted results from computational analysis, different SH species were observed to present different chemical constituents, and diverse effects on RA-relevant proteins at the biological level. CONCLUSIONS The chemical and biological properties of SO, SP and SG were different and distinctive. The systematic comparison between these three confusing Chinese herbs provides reliable characterization profiles to clarify the pharmacological substances in SH for the precise management of rheumatism/-related diseases in clinics.
Collapse
Affiliation(s)
- Qian Ru Zhang
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China; School of Pharmacy, Zunyi Medical University, Zunyi, Guizhou, China
| | - Zhang Feng Zhong
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China; Guangdong Key Laboratory for Research and Development of Natural Drugs, Guangdong Medical University, Zhanjiang, China
| | - Wei Sang
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Wei Xiong
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Hong Xun Tao
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Guan Ding Zhao
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Zhi Xin Li
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Qiu Shuo Ma
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Anfernee Kai Wing Tse
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yuan Jia Hu
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China.
| | - Hua Yu
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China; HKBU Shenzhen Research Center, Shenzhen, Guangdong, China; School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
| | - Yi Tao Wang
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| |
Collapse
|
9
|
Piir G, Kahn I, García-Sosa AT, Sild S, Ahte P, Maran U. Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:126001. [PMID: 30561225 PMCID: PMC6371683 DOI: 10.1289/ehp3264] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 10/19/2018] [Accepted: 11/07/2018] [Indexed: 05/31/2023]
Abstract
BACKGROUND Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented. OBJECTIVES The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs. METHODS A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots. RESULTS Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles. DISCUSSION Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.
Collapse
Affiliation(s)
- Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Iiris Kahn
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | | | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Priit Ahte
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| |
Collapse
|
10
|
Gozalbes R, Vicente de Julián-Ortiz J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.
Collapse
|
11
|
Miteva MA, Villoutreix BO. Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties. Mol Inform 2017; 36. [DOI: 10.1002/minf.201700008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 02/03/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Maria A. Miteva
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
| | - Bruno O. Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
| |
Collapse
|
12
|
Villoutreix B. Combining bioinformatics, chemoinformatics and experimental approaches to design chemical probes: Applications in the field of blood coagulation. ANNALES PHARMACEUTIQUES FRANÇAISES 2016; 74:253-66. [DOI: 10.1016/j.pharma.2016.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 03/21/2016] [Accepted: 03/21/2016] [Indexed: 11/08/2022]
|
13
|
Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts. Future Med Chem 2014; 6:2029-56. [DOI: 10.4155/fmc.14.137] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Conclusion: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.
Collapse
|
14
|
Liggi S, Drakakis G, Hendry AE, Hanson KM, Brewerton SC, Wheeler GN, Bodkin MJ, Evans DA, Bender A. Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application toXenopus laevisPhenotypic Readouts. Mol Inform 2013; 32:1009-24. [DOI: 10.1002/minf.201300102] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 08/06/2013] [Indexed: 12/20/2022]
|
15
|
Taboureau O, Baell JB, Fernández-Recio J, Villoutreix BO. Established and emerging trends in computational drug discovery in the structural genomics era. ACTA ACUST UNITED AC 2012; 19:29-41. [PMID: 22284352 DOI: 10.1016/j.chembiol.2011.12.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2011] [Revised: 12/05/2011] [Accepted: 12/08/2011] [Indexed: 12/01/2022]
Abstract
Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.
Collapse
Affiliation(s)
- Olivier Taboureau
- Center for Biological Sequences Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | | | | | | |
Collapse
|