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Wang Y, Wang P, Fan T, Ren T, Zhang N, Zhao L, Zhong R, Sun G. From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:134945. [PMID: 38905984 DOI: 10.1016/j.jhazmat.2024.134945] [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: 04/24/2024] [Revised: 06/03/2024] [Accepted: 06/15/2024] [Indexed: 06/23/2024]
Abstract
The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.
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Affiliation(s)
- Yutong Wang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Peng Wang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers, Beijing 100079, China
| | - Ting Ren
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, PR China.
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van Tilborg D, Brinkmann H, Criscuolo E, Rossen L, Özçelik R, Grisoni F. Deep learning for low-data drug discovery: Hurdles and opportunities. Curr Opin Struct Biol 2024; 86:102818. [PMID: 38669740 DOI: 10.1016/j.sbi.2024.102818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024]
Abstract
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches-which are notoriously 'data-hungry'-might fail to live up to their promise. Developing novel approaches to leverage the power of deep learning in low-data scenarios is sparking great attention, and future developments are expected to propel the field further. This mini-review provides an overview of recent low-data-learning approaches in drug discovery, analyzing their hurdles and advantages. Finally, we venture to provide a forecast of future research directions in low-data learning for drug discovery.
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Affiliation(s)
- Derek van Tilborg
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Princetonlaan 6, 3584 CB, Utrecht, the Netherlands. https://twitter.com/DerekvTilborg
| | - Helena Brinkmann
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. https://twitter.com/hlnbrkmnn
| | - Emanuele Criscuolo
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. https://twitter.com/emanuelecriscu9
| | - Luke Rossen
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. https://twitter.com/molecular_ml
| | - Rıza Özçelik
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Princetonlaan 6, 3584 CB, Utrecht, the Netherlands. https://twitter.com/Rza_ozcelik
| | - Francesca Grisoni
- Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands; Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Princetonlaan 6, 3584 CB, Utrecht, the Netherlands.
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Development of a High-Accuracy Statistical Model to Identify the Key Parameter for Methane Adsorption in Metal-Organic Frameworks. ANALYTICA 2022. [DOI: 10.3390/analytica3030024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The geometrical and topological features of metal-organic frameworks (MOFs) play an important role in determining their ability to capture and store methane (CH4). Methane is a greenhouse gas that has been shown to be more dangerous in terms of contributing to global warming than carbon dioxide (CO2), especially in the first 20 years of its release into the atmosphere. Its accelerated emission increases the rate of global temperature increase and needs to be addressed immediately. Adsorption processes have been shown to be effective and efficient in mitigating methane emissions from the atmosphere by providing an enormous surface area for methane storage. Among all the adsorbents, MOFs were shown to be the best adsorbents for methane adsorption due to their higher favorable steric interactions, the presence of binding sites such as open metal sites, and hydrophobic pockets. These features may not necessarily be present in carbonaceous materials and zeolites. Although many studies have suggested that the main reason for the increased storage efficiencies in terms of methane in the MOFs is the high surface area, there was some evidence in certain research works that methane storage performance, as measured by uptakes and deliveries in gravimetric and volumetric units, was higher for certain MOFs with a lower surface area. This prompted us to find out the most significant property of the MOF, whether it be material-based or pore-based, that has the maximum influence on methane uptake and delivery, using a comprehensive statistical approach that has not previously been employed in the methane storage literature. The approach in our study employed various chemometric techniques, including simple and multiple linear regression (SLR and MLR), combined with different types of multicollinearity diagnostics, partial correlations, standardized coefficients, and changes in regression coefficient estimates and their standard errors, applied to both the SLR and MLR models. The main advantages of this statistical approach are that it is quicker, provides a deeper insight into experimental data, and highlights a single, most important, parameter for MOF design and tuning that can predict and maximize the output storage and capture performance. The significance of our approach is that it was modeled purely based on experimental data, which will capture the real system, as opposed to the molecular simulations employed previously in the literature. Our model included data from ~80 MOFs and eight properties related to the material, pore, and thermodynamics (isosteric adsorption energy). Successful attempts to model the methane sorption process have previously been conducted using thermodynamic approaches and by developing adsorption performance indicators, but these are either too complex or time-consuming and their data covers fewer than 10 MOFs and a maximum of three MOF properties. By comparing the statistical metrics between the models, the most important and statistically significant property of the MOF was determined, which will be crucial when designing MOFs for use in storing and delivering methane.
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Halder AK, Haghbakhsh R, Voroshylova IV, Duarte ARC, Cordeiro MNDS. Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27154896. [PMID: 35956845 PMCID: PMC9370217 DOI: 10.3390/molecules27154896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022]
Abstract
Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including ‘mixtures-out’- and ‘compounds-out’-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.
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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 and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, WB, India
- Correspondence: (A.K.H.); (M.N.D.S.C.); Tel.: +351-2240-2502 (M.N.D.S.C.)
| | - Reza Haghbakhsh
- Department of Chemical Engineering, Faculty of Engineering, University of Isfahan, Isfahan 81746-73441, Iran;
- LAQV@REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, 2829-516 Caparica, Portugal;
| | - Iuliia V. Voroshylova
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal;
| | - Ana Rita C. Duarte
- LAQV@REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, 2829-516 Caparica, Portugal;
| | - Maria Natalia D. S. Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal;
- Correspondence: (A.K.H.); (M.N.D.S.C.); Tel.: +351-2240-2502 (M.N.D.S.C.)
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Halder AK, Ambure P, Perez-Castillo Y, Cordeiro MND. Turning deep-eutectic solvents into value-added products for CO2 capture: A desirability-based virtual screening study. J CO2 UTIL 2022. [DOI: 10.1016/j.jcou.2022.101926] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures. Molecules 2021; 26:molecules26195779. [PMID: 34641322 PMCID: PMC8510218 DOI: 10.3390/molecules26195779] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/19/2021] [Accepted: 09/21/2021] [Indexed: 12/26/2022] Open
Abstract
Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.
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Nigam A, Pollice R, Hurley MFD, Hickman RJ, Aldeghi M, Yoshikawa N, Chithrananda S, Voelz VA, Aspuru-Guzik A. Assigning confidence to molecular property prediction. Expert Opin Drug Discov 2021; 16:1009-1023. [DOI: 10.1080/17460441.2021.1925247] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | | | - Riley J. Hickman
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, University Ave Suite 710, Toronto, Canada
| | - Naruki Yoshikawa
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | | | | | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, University Ave Suite 710, Toronto, Canada
- Canadian Institute for Advanced Research (CIFAR), University Ave, Toronto, Canada
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8
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Scior T, Abdallah HH, Salvador-Atonal K, Laufer S. Dapsone is not a Pharmacodynamic Lead Compound for its Aryl Derivatives. Curr Comput Aided Drug Des 2021; 16:327-339. [PMID: 32507104 DOI: 10.2174/1573409915666191010104527] [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: 06/14/2019] [Revised: 09/11/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND The relatedness between the linear equations of thermodynamics and QSAR was studied thanks to the recently elucidated crystal structure complexes between sulfonamide pterin conjugates and dihydropteroate synthase (DHPS) together with a published set of thirty- six synthetic dapsone derivatives with their reported entropy-driven activity data. Only a few congeners were slightly better than dapsone. OBJECTIVE Our study aimed at demonstrating the applicability of thermodynamic QSAR and to shed light on the mechanistic aspects of sulfone binding to DHPS. METHODS To this end ligand docking to DHPS, quantum mechanical properties, 2D- and 3D-QSAR as well as Principle Component Analysis (PCA) were carried out. RESULTS The short aryl substituents of the docked pterin-sulfa conjugates were outward oriented into the solvent space without interacting with target residues which explains why binding enthalpy (ΔH) did not correlate with potency. PCA revealed how chemically informative descriptors are evenly loaded on the first three PCs (interpreted as ΔG, ΔH and ΔS), while chemically cryptic ones reflected higher dimensional (complex) loadings. CONCLUSION It is safe to utter that synthesis efforts to introduce short side chains for aryl derivatization of the dapsone scaffold have failed in the past. On theoretical grounds we provide computed evidence why dapsone is not a pharmacodynamic lead for drug profiling because enthalpic terms do not change significantly at the moment of ligand binding to target.
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Affiliation(s)
- Thomas Scior
- Chemical Science Faculty, Benemerita Universidad Autonoma de Puebla, C.P. 72570, Puebla, Mexico
| | - Hassan H Abdallah
- Chemistry Department, College of Education, Salahaddin University, Erbil, Iraq.,Pharmacy School, University Sains Malaysia, USM, 11800, Penang, Malaysia
| | - Kenia Salvador-Atonal
- Chemical Science Faculty, Benemerita Universidad Autonoma de Puebla, C.P. 72570, Puebla, Mexico
| | - Stefan Laufer
- Pharmazeutisches Institut, Eberhard Karls Universität Tübingen, Tübingen, Germany
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Hao Y, Sun G, Fan T, Tang X, Zhang J, Liu Y, Zhang N, Zhao L, Zhong R, Peng Y. In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse. JOURNAL OF HAZARDOUS MATERIALS 2020; 399:122981. [PMID: 32534390 DOI: 10.1016/j.jhazmat.2020.122981] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/14/2020] [Accepted: 05/16/2020] [Indexed: 06/11/2023]
Abstract
Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.
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Affiliation(s)
- Yuxing Hao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Guohui Sun
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Xiaoyu Tang
- College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Jing Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Yongdong Liu
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Na Zhang
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental and Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, PR China.
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, PR China.
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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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11
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Nekrasova NA, Kurbatova SV. Comparative Study of Quinolines and Tetrahydroquinolines Sorption on Various Sorbents from Water-Acetonitrile Solutions. J Chromatogr Sci 2019; 57:369-380. [PMID: 30615111 DOI: 10.1093/chromsci/bmy111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 11/26/2018] [Accepted: 12/04/2018] [Indexed: 11/14/2022]
Abstract
The retention of 16 quinoline and tetrahydroquinoline derivatives was investigated under liquid chromatography conditions using porous graphitized carbon (PGC), octadecyl silica (ODS) and hypercrosslinked polystyrene (HCLP) stationary phases. For most of the analytes, retention on PGC was greater than on ODS, while retention on HCLP was even greater than on both ODS and PGC. The non-linearity of retention dependencies on acetonitrile content in the eluent was observed for compounds containing carboxy, hydrazo and methoxy groups. The relationships between quinolines structure and their retention factors were investigated. It was found that sorption on different sorbents correlated with different descriptors. Retention on ODS was found to be highly correlated with lipophilicity only while on HCLP it depended on both lipophilicity and polarizability of the sorbates. The feature of PGC was a good correlation of retention factors with topological and geometrical parameters. Additionally, ability of PGC to form OH…π bonds with hydroxymethylquinolines was found. The observed regularities allow one to rationally optimize the chromatographic analysis of structurally similar compounds, which is very important for bioactive substances.
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Affiliation(s)
- Nadezhda A Nekrasova
- Department of Physical Chemistry and Chromatography, Samara National Research University Named after Academician S.P. Korolev, 34 Moskovskoye Shosse, Samara, Russia.,Samara Center for Theoretical Material Science, 34 Moskovskoye Shosse, Samara, Russia
| | - Svetlana V Kurbatova
- Department of Physical Chemistry and Chromatography, Samara National Research University Named after Academician S.P. Korolev, 34 Moskovskoye Shosse, Samara, Russia
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12
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Datta S, Dev VA, Eden MR. Using correlation based adaptive LASSO algorithm to develop QSPR of antitumour agents for DNA–drug binding prediction. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.08.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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13
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De Benedetti PG, Fanelli F. Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery. Drug Discov Today 2018; 23:1396-1406. [PMID: 29574212 DOI: 10.1016/j.drudis.2018.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 02/22/2018] [Accepted: 03/19/2018] [Indexed: 11/22/2022]
Abstract
Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling.
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Affiliation(s)
- Pier G De Benedetti
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy.
| | - Francesca Fanelli
- Department of Life Sciences, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy; Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, via Campi 103, 41125 Modena, Italy
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14
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Minenkov Y, Sharapa DI, Cavallo L. Application of Semiempirical Methods to Transition Metal Complexes: Fast Results but Hard-to-Predict Accuracy. J Chem Theory Comput 2018; 14:3428-3439. [DOI: 10.1021/acs.jctc.8b00018] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yury Minenkov
- Moscow Institute
of Physics and Technology, Institutskiy Pereulok 9, Dolgoprudny, Moscow Region 141700, Russia
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Dmitry I. Sharapa
- Chair of Theoretical Chemistry and Interdisciplinary Center for Molecular Materials, Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstraße3, 91058 Erlangen, Germany
| | - Luigi Cavallo
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Karlberg M, von Stosch M, Glassey J. Exploiting mAb structure characteristics for a directed QbD implementation in early process development. Crit Rev Biotechnol 2018. [DOI: 10.1080/07388551.2017.1421899] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Micael Karlberg
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK
| | - Moritz von Stosch
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK
| | - Jarka Glassey
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK
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16
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Nekrasova NA, Kurbatova SV. Quantitative structure–chromatographic retention correlations of quinoline derivatives. J Chromatogr A 2017; 1492:55-60. [DOI: 10.1016/j.chroma.2017.02.063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 02/07/2017] [Accepted: 02/26/2017] [Indexed: 12/19/2022]
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17
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de Souza AS, Ferreira LG, Andricopulo AD. 2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.
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Affiliation(s)
- Anacleto S. de Souza
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil
| | - Leonardo G. Ferreira
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil
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18
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Scior T, Lozano-Aponte J, Ajmani S, Hernández-Montero E, Chávez-Silva F, Hernández-Núñez E, Moo-Puc R, Fraguela-Collar A, Navarrete-Vázquez G. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight. Curr Comput Aided Drug Des 2016; 11:21-31. [PMID: 25872791 PMCID: PMC5396257 DOI: 10.2174/1573409911666150414145937] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Revised: 02/02/2015] [Accepted: 04/03/2015] [Indexed: 12/29/2022]
Abstract
In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target.
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Affiliation(s)
- Thomas Scior
- Department of Pharmacy, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Ciudad Universitaria, Edificio 105 C/106, C.P. 72570 Puebla, PUE., Mexico.
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García-Jacas CR, Contreras-Torres E, Marrero-Ponce Y, Pupo-Meriño M, Barigye SJ, Cabrera-Leyva L. Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets. J Cheminform 2016; 8:10. [PMID: 26925168 PMCID: PMC4768433 DOI: 10.1186/s13321-016-0122-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 02/09/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. RESULTS To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. CONCLUSIONS As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.
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Affiliation(s)
- César R García-Jacas
- Escuela de Sistemas y Computación, Pontificia Universidad Católica del Ecuador Sede Esmeraldas (PUCESE), Esmeraldas, Ecuador ; Grupo de Investigación de Bioinformática, Centro de Estudio de Matemática Computacional (CEMC), Universidad de las Ciencias Informáticas, La Habana, Cuba ; Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador ; Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y vía Interoceánica, 17-1200-841 Quito, Ecuador
| | - Ernesto Contreras-Torres
- Departamento de Técnicas de Programación, Facultad 6, Universidad de las Ciencias Informáticas, La Habana, Cuba
| | - Yovani Marrero-Ponce
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador ; Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y vía Interoceánica, 17-1200-841 Quito, Ecuador ; Escuela de Medicina, Colegio de Ciencias de la Salud, Edificio de Especialidades Médicas, Hospital de los Valles, Universidad San Francisco de Quito (USFQ), Av. Interoceánica Km 12 ½ - Cumbayá, Quito, Ecuador
| | - Mario Pupo-Meriño
- Grupo de Investigación de Bioinformática, Centro de Estudio de Matemática Computacional (CEMC), Universidad de las Ciencias Informáticas, La Habana, Cuba
| | - Stephen J Barigye
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador ; Departamento de Química, Universidade Federal de Lavras, UFLA, Caixa Postal 3037, Lavras, MG 37200-000 Brazil
| | - Lisset Cabrera-Leyva
- Escuela de Sistemas y Computación, Pontificia Universidad Católica del Ecuador Sede Esmeraldas (PUCESE), Esmeraldas, Ecuador ; Grupo de Investigación de Inteligencia Artificial (AIRES), Facultad de Informática, Universidad de Camagüey, Camagüey, Cuba
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21
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Finkelmann AR, Göller AH, Schneider G. Robust molecular representations for modelling and design derived from atomic partial charges. Chem Commun (Camb) 2016; 52:681-4. [DOI: 10.1039/c5cc07887c] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Ab initio partial charge schemes are identified for molecular modelling purposes, and potential pitfalls of their application are discussed.
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Affiliation(s)
- A. R. Finkelmann
- Department of Chemistry and Applied Biosciences
- ETH Zürich
- 8093 Zürich
- Switzerland
| | - A. H. Göller
- Bayer Pharma AG
- Global Drug Discovery
- D-42096 Wuppertal
- Germany
| | - G. Schneider
- Department of Chemistry and Applied Biosciences
- ETH Zürich
- 8093 Zürich
- Switzerland
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22
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Čolović J, Kalinić M, Vemić A, Erić S, Malenović A. Investigation into the phenomena affecting the retention behavior of basic analytes in chaotropic chromatography: Joint effects of the most relevant chromatographic factors and analytes’ molecular properties. J Chromatogr A 2015; 1425:150-7. [DOI: 10.1016/j.chroma.2015.11.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 11/06/2015] [Accepted: 11/08/2015] [Indexed: 11/17/2022]
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23
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Nieto-Draghi C, Fayet G, Creton B, Rozanska X, Rotureau P, de Hemptinne JC, Ungerer P, Rousseau B, Adamo C. A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes. Chem Rev 2015; 115:13093-164. [PMID: 26624238 DOI: 10.1021/acs.chemrev.5b00215] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Carlos Nieto-Draghi
- IFP Energies nouvelles , 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
| | - Guillaume Fayet
- INERIS, Parc Technologique Alata, BP2 , 60550 Verneuil-en-Halatte, France
| | - Benoit Creton
- IFP Energies nouvelles , 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
| | - Xavier Rozanska
- Materials Design S.A.R.L. , 18, rue de Saisset, 92120 Montrouge, France
| | - Patricia Rotureau
- INERIS, Parc Technologique Alata, BP2 , 60550 Verneuil-en-Halatte, France
| | | | - Philippe Ungerer
- Materials Design S.A.R.L. , 18, rue de Saisset, 92120 Montrouge, France
| | - Bernard Rousseau
- Laboratoire de Chimie-Physique, Université Paris Sud , UMR 8000 CNRS, Bât. 349, 91405 Orsay Cedex, France
| | - Carlo Adamo
- Institut de Recherche Chimie Paris, PSL Research University, CNRS, Chimie Paristech , 11 rue P. et M. Curie, F-75005 Paris, France.,Institut Universitaire de France , 103 Boulevard Saint Michel, F-75005 Paris, France
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24
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Wang T, Wu MB, Lin JP, Yang LR. Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov 2015; 10:1283-300. [DOI: 10.1517/17460441.2015.1083006] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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25
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Theoretical description of 2D-cluster formation of nonionic surfactants at the air/water interface. Colloid Polym Sci 2015. [DOI: 10.1007/s00396-015-3630-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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26
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Vandewiele NM, Van de Vijver R, Van Geem KM, Reyniers MF, Marin GB. Symmetry calculation for molecules and transition states. J Comput Chem 2014; 36:181-92. [DOI: 10.1002/jcc.23788] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 10/06/2014] [Accepted: 10/10/2014] [Indexed: 12/18/2022]
Affiliation(s)
- Nick M. Vandewiele
- Laboratory for Chemical Technology; Universiteit Gent; Technologiepark 914 B-9052 Gent Belgium
| | - Ruben Van de Vijver
- Laboratory for Chemical Technology; Universiteit Gent; Technologiepark 914 B-9052 Gent Belgium
| | - Kevin M. Van Geem
- Laboratory for Chemical Technology; Universiteit Gent; Technologiepark 914 B-9052 Gent Belgium
| | | | - Guy B. Marin
- Laboratory for Chemical Technology; Universiteit Gent; Technologiepark 914 B-9052 Gent Belgium
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Arabshahi HJ, Leung E, Barker D, Reynisson J. The development of thieno[2,3-b]pyridine analogues as anticancer agents applying in silico methods. MEDCHEMCOMM 2014. [DOI: 10.1039/c3md00320e] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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28
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Norinder U, Boström H. Representing descriptors derived from multiple conformations as uncertain features for machine learning. J Mol Model 2013; 19:2679-85. [DOI: 10.1007/s00894-013-1806-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Accepted: 02/11/2013] [Indexed: 10/27/2022]
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Varmuza K, Filzmoser P, Dehmer M. Multivariate linear QSPR/QSAR models: Rigorous evaluation of variable selection for PLS. Comput Struct Biotechnol J 2013; 5:e201302007. [PMID: 24688700 PMCID: PMC3962078 DOI: 10.5936/csbj.201302007] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Revised: 01/22/2013] [Accepted: 01/28/2013] [Indexed: 11/22/2022] Open
Abstract
Basic chemometric methods for making empirical regression models for QSPR/QSAR are briefly described from a user's point of view. Emphasis is given to PLS regression, simple variable selection and a careful and cautious evaluation of the performance of PLS models by repeated double cross validation (rdCV). A demonstration example is worked out for QSPR models that predict gas chromatographic retention indices (values between 197 and 504 units) of 209 polycyclic aromatic compounds (PAC) from molecular descriptors generated by Dragon software. Most favorable models were obtained from data sets containing also descriptors from 3D structures with all H-atoms (computed by Corina software), using stepwise variable selection (reducing 2688 descriptors to a subset of 22). The final QSPR model has typical prediction errors for the retention index of ±12 units (95% tolerance interval, for test set objects). Programs and data are provided as supplementary material for the open source R software environment.
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Affiliation(s)
- Kurt Varmuza
- Institute of Chemical Engineering, Vienna University of Technology, Austria ; These authors contributed equally to this work
| | - Peter Filzmoser
- Department of Statistics and Probability Theory, Vienna University of Technology, Austria ; These authors contributed equally to this work
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT - The Health and Life Sciences University, Hall in Tyrol, Austria ; These authors contributed equally to this work
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