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Martinez-Mayorga K, Rosas-Jiménez JG, Gonzalez-Ponce K, López-López E, Neme A, Medina-Franco JL. The pursuit of accurate predictive models of the bioactivity of small molecules. Chem Sci 2024; 15:1938-1952. [PMID: 38332817 PMCID: PMC10848664 DOI: 10.1039/d3sc05534e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.
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Affiliation(s)
- Karina Martinez-Mayorga
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José G Rosas-Jiménez
- Department of Theoretical Biophysics, IMPRS on Cellular Biophysics Max-von-Laue Strasse 3 Frankfurt am Main 60438 Germany
| | - Karla Gonzalez-Ponce
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
| | - Edgar López-López
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute Mexico City 07000 Mexico
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
| | - Antonio Neme
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
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2
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Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood) 2023; 248:1952-1973. [PMID: 38057999 PMCID: PMC10798180 DOI: 10.1177/15353702231209421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Meng Song
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
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Banerjee A, Roy K. Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2023; 25:1626-1644. [PMID: 37682520 DOI: 10.1039/d3em00322a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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4
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Banerjee A, Roy K. On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points. Chem Res Toxicol 2023; 36:446-464. [PMID: 36811528 DOI: 10.1021/acs.chemrestox.2c00374] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The novel quantitative read-across structure-activity relationship (q-RASAR) approach uses read-across-derived similarity functions in the quantitative structure-activity relationship (QSAR) modeling framework in a unique way for supervised model generation. The aim of this study is to explore how this workflow enhances the external (test set) prediction quality of conventional QSAR models by the incorporation of some novel similarity-based functions as additional descriptors using the same level of chemical information. To establish this, five different toxicity data sets, for which QSAR models were reported previously, have been considered in the q-RASAR modeling exercise, which uses chemical similarity-derived measures. The identical sets of chemical features along with the same compositions of training and test sets as reported previously were used in the present analysis for ease of comparison. The RASAR descriptors were calculated based on a chosen similarity measure with the default setting of relevant hyperparameter(s) and were then clubbed with the original structural and physicochemical descriptors, and the number of selected features was further optimized by employing a grid search technique applied on the respective training sets. These features were then used to develop multiple linear regression (MLR) q-RASAR models that show enhanced predictivity as compared to the QSAR models developed previously. Moreover, various other ML algorithms like support vector machine (SVM), linear SVM, random forest, partial least squares, and ridge regression were also employed using the same feature combinations as used in the MLR models to compare the prediction qualities. The q-RASAR models for five different data sets possess at least one of the RASAR descriptors, RA function, gm, and average similarity, suggesting that these are important determinants of similarities that contribute to the development of predictive q-RASAR models, as also evident from the SHAP analysis of the models.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
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5
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Luna IS, Souza TAD, da Silva MS, Franca Rodrigues KAD, Scotti L, Scotti MT, Mendonça-Junior FJB. Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents. Eur J Med Chem 2023; 250:115223. [PMID: 36848847 DOI: 10.1016/j.ejmech.2023.115223] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/23/2023]
Abstract
The leishmaniasis is a neglected disease caused by a group of protozoan parasites from the genus Leishmania whose treatment is limited, obsolete, toxic, and ineffective in certain cases. These characteristics motivate researchers worldwide to plan new therapeutic alternatives for the treatment of leishmaniasis, where the use of cheminformatics tools applied to computer-assisted drug design has allowed research to make great advances in the search for new drugs candidates. In this study, a series of 2-amino-thiophene (2-AT) derivatives was screened virtually using QSAR tools, ADMET filters and prediction models, allowing direct the synthesis of compounds, which were evaluated in vitro against promastigotes and axenic amastigotes of Leishmania amazonensis. The combination of different descriptors and machine learning methods led to obtaining robust and predictive QSAR models, which was obtained from a dataset composed of 1862 compounds extracted from the ChEMBL database, with correct classification rates ranging from 0.53 (for amastigotes) to 0.91 (for promastigotes), allowing to select eleven 2-AT derivatives, which do not violate Lipinski's rules, exhibit good druglikeness, and with probability ≤70% of potential activity against the two evolutionary forms of the parasite. All compounds were properly synthesized and 8 of them were shown to be active at least against one of the evolutionary forms of the parasite with IC50 values lower than 10 μM, being more active than the reference drug meglumine antimoniate, and showing low or no citotoxicity against macrophage J774.A1 for the most part. Compounds 8CN and DCN-83, respectively, are the most active against promastigote and amastigote forms, with IC50 values of 1.20 and 0.71 μM, and selectivity indexes (SI) of 36.58 and 119.33. Structure Activity Relationship (SAR) study was carried out and allowed to identify some favorable and/or essential substitution patterns for the leishmanial activity of 2-AT derivatives. Taken together, these findings demonstrate that the use of ligand-based virtual screening proved to be quite effective and saved time, effort, and money in the selection of potential anti-leishmanial agents, and confirm, once again that 2-AT derivatives are promising hit compounds for the development of new anti-leishmanial agents.
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Affiliation(s)
- Isadora Silva Luna
- Laboratory of Synthesis and Drug Delivery, State University of Paraiba, João Pessoa, PB, Brazil; Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Thalisson Amorim de Souza
- Multiuser Laboratory Center of Characterization and Analysis, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Marcelo Sobral da Silva
- Multiuser Laboratory Center of Characterization and Analysis, Federal University of Paraiba, João Pessoa, PB, Brazil
| | | | - Luciana Scotti
- Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil
| | - Francisco Jaime Bezerra Mendonça-Junior
- Laboratory of Synthesis and Drug Delivery, State University of Paraiba, João Pessoa, PB, Brazil; Post-Graduation Program in Natural and Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa, PB, Brazil.
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6
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Tullius Scotti M, Herrera-Acevedo C, Barros de Menezes RP, Martin HJ, Muratov EN, Ítalo de Souza Silva Á, Faustino Albuquerque E, Ferreira Calado L, Coy-Barrera E, Scotti L. MolPredictX: Online Biological Activity Predictions by Machine Learning Models. Mol Inform 2022; 41:e2200133. [PMID: 35961924 DOI: 10.1002/minf.202200133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/12/2022] [Indexed: 01/05/2023]
Abstract
Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in-house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars-CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.
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Affiliation(s)
- Marcus Tullius Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Chonny Herrera-Acevedo
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil.,Department of Chemical Engineering, Universidad ECCI, Carrera 19 # 49-20, 111311, Bogotá D.C., Colombia
| | - Renata Priscila Barros de Menezes
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ávilla Ítalo de Souza Silva
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Emmanuella Faustino Albuquerque
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Lucas Ferreira Calado
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Cajicá, 250247, Colombia
| | - Luciana Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
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7
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Hochuli J, Jain S, Melo-Filho C, Sessions ZL, Bobrowski T, Choe J, Zheng J, Eastman R, Talley DC, Rai G, Simeonov A, Tropsha A, Muratov EN, Baljinnyam B, Zakharov AV. Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents. ACS Pharmacol Transl Sci 2022; 5:468-478. [PMID: 35821746 PMCID: PMC9236207 DOI: 10.1021/acsptsci.2c00049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for acute treatment of the disease. We investigate whether compounds that bind the human angiotensin-converting enzyme 2 (ACE2) protein can decrease SARS-CoV-2 replication without impacting ACE2's natural enzymatic function. Initial screening of a diversity library resulted in hit compounds active in an ACE2-binding assay, which showed little inhibition of ACE2 enzymatic activity (116 actives, success rate ∼4%), suggesting they were allosteric binders. Subsequent application of in silico techniques boosted success rates to ∼14% and resulted in 73 novel confirmed ACE2 binders with K d values as low as 6 nM. A subsequent SARS-CoV-2 assay revealed that five of these compounds inhibit the viral life cycle in human cells. Further effort is required to completely elucidate the antiviral mechanism of these ACE2-binders, but they present a valuable starting point for both the development of acute treatments for COVID-19 and research into the host-directed therapy.
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Affiliation(s)
- Joshua
E. Hochuli
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
- Curriculum
in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Sankalp Jain
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Cleber Melo-Filho
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L. Sessions
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Tesia Bobrowski
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jun Choe
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Johnny Zheng
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Richard Eastman
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Daniel C. Talley
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Ganesha Rai
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Anton Simeonov
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Alexander Tropsha
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N. Muratov
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Bolormaa Baljinnyam
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Alexey V. Zakharov
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
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8
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Orosz Á, Héberger K, Rácz A. Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets. Front Chem 2022; 10:852893. [PMID: 35755260 PMCID: PMC9214226 DOI: 10.3389/fchem.2022.852893] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/14/2022] [Indexed: 01/12/2023] Open
Abstract
The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.
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Affiliation(s)
- Álmos Orosz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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9
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Barros de Menezes RP, Fechine Tavares J, Kato MJ, da Rocha Coelho FA, Sousa Dos Santos AL, da Franca Rodrigues KA, Sessions ZL, Muratov EN, Scotti L, Tullius Scotti M. Natural Products from Annonaceae as Potential Antichagasic Agents. ChemMedChem 2022; 17:e202200196. [PMID: 35678042 DOI: 10.1002/cmdc.202200196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/06/2022] [Indexed: 11/12/2022]
Abstract
Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.
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Affiliation(s)
- Renata Priscila Barros de Menezes
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Josean Fechine Tavares
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Massuo Jorge Kato
- Instituto de Química, Universidade de São Paulo, 05508-000, São Paulo, SP, Brazil
| | | | | | | | - Zoe L Sessions
- Molecular Modeling Lab, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, 27599, NC, USA
| | - Eugene N Muratov
- Molecular Modeling Lab, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, 27599, NC, USA
| | - Luciana Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Programa de Pós-Graduação de Produtos Naturais e Sintéticos Bioativos, Universidade federal da Paraíba, 58051-900, João Pessoa, PB, Brazil
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10
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Hochuli JE, Jain S, Melo-filho C, Sessions ZL, Bobrowski T, Choe J, Zheng J, Eastman R, Talley DC, Rai G, Simeonov A, Tropsha A, Muratov EN, Baljinnyam B, Zakharov AV. Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.. [PMID: 35313579 PMCID: PMC8936107 DOI: 10.1101/2022.03.15.484484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AbstractThe COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.Abstract FigureTOC Graphic: Overall study design.
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11
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de Menezes RPB, Viana JDO, Muratov E, Scotti L, Scotti MT. Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity. Curr Issues Mol Biol 2022; 44:383-408. [PMID: 35723407 PMCID: PMC8929062 DOI: 10.3390/cimb44010028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/08/2022] [Accepted: 01/09/2022] [Indexed: 11/28/2022] Open
Abstract
Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.
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Affiliation(s)
- Renata Priscila Barros de Menezes
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Jéssika de Oliveira Viana
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA;
| | - Luciana Scotti
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural Synthetic Bioactive Products, Federal University of Paraiba, João Pessoa 58051-900, PB, Brazil; (R.P.B.d.M.); (J.d.O.V.); (L.S.)
- Correspondence: ; Tel.: +55-83-998690415
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12
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Silva AC, Borba JV, Alves VM, Hall SU, Furnham N, Kleinstreuer N, Muratov E, Tropsha A, Andrade CH. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2021; 1. [PMID: 35935266 PMCID: PMC9355119 DOI: 10.1016/j.ailsci.2021.100028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68–0.88), sensitivity (SE of 0.61–0.84), positive predictive value (PPV of 0.65–0.90), specificity (SP of 0.56–0.91), and negative predictive value (NPV of 0.68–0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds’ irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds
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13
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Jain S, Talley DC, Baljinnyam B, Choe J, Hanson Q, Zhu W, Xu M, Chen CZ, Zheng W, Hu X, Shen M, Rai G, Hall MD, Simeonov A, Zakharov AV. Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants. ACS Pharmacol Transl Sci 2021; 4:1675-1688. [PMID: 34608449 PMCID: PMC8482323 DOI: 10.1021/acsptsci.1c00176] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Indexed: 11/30/2022]
Abstract
The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.
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Affiliation(s)
- Sankalp Jain
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Daniel C. Talley
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Bolormaa Baljinnyam
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Jun Choe
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Quinlin Hanson
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Wei Zhu
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Miao Xu
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Catherine Z. Chen
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Wei Zheng
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Xin Hu
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Min Shen
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Ganesha Rai
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Matthew D. Hall
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Anton Simeonov
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexey V. Zakharov
- National Center for Advancing
Translational Sciences (NCATS), National
Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
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14
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de Morais E Silva L, Alves VM, Dantas ERB, Scotti L, Lopes WS, Muratov EN, Scotti MT. Chemical safety assessment of transformation products of landfill leachate formed during the Fenton process. JOURNAL OF HAZARDOUS MATERIALS 2021; 419:126438. [PMID: 34182425 DOI: 10.1016/j.jhazmat.2021.126438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 06/13/2023]
Abstract
Organic chemicals identified in raw landfill leachate (LL) and their transformation products (TPs), formed during Fenton treatment, were analyzed for chemical safety following REACH guidelines. The raw LL was located in the metropolitan region of Campina Grande, in northeast Brazil. We elucidated 197 unique chemical structures, including 154 compounds that were present in raw LL and 82 compounds that were detected in the treated LL, totaling 39 persistent compounds and 43 TPs. In silico models were developed to identify and prioritize the potential level of hazard/risk these compounds pose to the environment and society. The models revealed that the Fenton process improved the biodegradability of TPs. Still, a slight increase in ecotoxicological effects was observed among the compounds in treated LL compared with those present in raw LL. No differences were observed for aryl hydrocarbon receptor (AhR) and antioxidant response element (ARE) mutagenicity. Similar behavior among both raw and treated LL samples was observed for biodegradability; Tetrahymena pyriformis, Daphnia magna, Pimephales promelas and ARE, AhR, and Ames mutagenicity. Overall, our results suggest that raw and treated LL samples have similar activity profiles for all endpoints other than biodegradability.
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Affiliation(s)
- Luana de Morais E Silva
- Post-Graduate Program in Science and Environmental Technology, Department of Sanitary and Environmental Engineering, State University of Paraíba, 58429-500 Campina Grande, Paraíba, Brazil
| | - Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Edilma R B Dantas
- Post-Graduate Program in Science and Environmental Technology, Department of Sanitary and Environmental Engineering, State University of Paraíba, 58429-500 Campina Grande, Paraíba, Brazil
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900 João Pessoa, Paraíba, Brazil; Teaching and Research Management - University Hospital, Federal University of Paraíba-Campus I, 58051-970 João Pessoa, Paraíba, Brazil
| | - Wilton Silva Lopes
- Post-Graduate Program in Science and Environmental Technology, Department of Sanitary and Environmental Engineering, State University of Paraíba, 58429-500 Campina Grande, Paraíba, Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900 João Pessoa, Paraíba, Brazil
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900 João Pessoa, Paraíba, Brazil.
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15
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Rácz A, Bajusz D, Miranda-Quintana RA, Héberger K. Machine learning models for classification tasks related to drug safety. Mol Divers 2021; 25:1409-1424. [PMID: 34110577 PMCID: PMC8342376 DOI: 10.1007/s11030-021-10239-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022]
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | | | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
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16
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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17
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Serafim MS, Gertrudes JC, Costa DM, Oliveira PR, Maltarollo VG, Honorio KM. Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates. Biosci Rep 2021; 41:BSR20202616. [PMID: 33624754 PMCID: PMC7982772 DOI: 10.1042/bsr20202616] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/15/2021] [Accepted: 02/23/2021] [Indexed: 01/18/2023] Open
Abstract
Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.
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Affiliation(s)
- Mateus S.M. Serafim
- Department of Microbiology, Biological Sciences Institute, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Jadson C. Gertrudes
- Department of Computer Science, Federal University of Ouro Preto (UFOP), Ouro Preto, MG, Brazil
| | - Débora M.A. Costa
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Patricia R. Oliveira
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
| | - Vinicius G. Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Kathia M. Honorio
- School of Arts, Sciences and Humanities, University of São Paulo (USP), 03828-000, São Paulo, SP, Brazil
- Center for Natural and Human Sciences, Federal University of ABC (UFABC), Santo Andre, SP, Brazil
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18
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Sánchez-Cruz N, Medina-Franco JL. Epigenetic Target Fishing with Accurate Machine Learning Models. J Med Chem 2021; 64:8208-8220. [PMID: 33770434 DOI: 10.1021/acs.jmedchem.1c00020] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.
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Affiliation(s)
- Norberto Sánchez-Cruz
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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19
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SHIMIZU N, KANEKO H. Constructing Regression Models with High Prediction Accuracy and Interpretability Based on Decision Tree and Random Forests. JOURNAL OF COMPUTER CHEMISTRY-JAPAN 2021. [DOI: 10.2477/jccj.2020-0021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Naoto SHIMIZU
- Department of Applied Chemistry, School of Science and Technology, Meiji University
| | - Hiromasa KANEKO
- Department of Applied Chemistry, School of Science and Technology, Meiji University
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20
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Soares Rodrigues GC, Maia MDS, Silva Cavalcanti AB, Costa Barros RP, Scotti L, Cespedes-Acuña CL, Muratov EN, Scotti MT. Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata. Food Chem Toxicol 2020; 147:111899. [PMID: 33279675 DOI: 10.1016/j.fct.2020.111899] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/25/2020] [Accepted: 11/30/2020] [Indexed: 11/18/2022]
Abstract
Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.
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Affiliation(s)
- Gabriela Cristina Soares Rodrigues
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Mayara Dos Santos Maia
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Renata Priscila Costa Barros
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Luciana Scotti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Carlos L Cespedes-Acuña
- Plant biochemistry and phytochemical ecotoxicology lab. Departamento de Ciencias Basicas, Facultad de Ciencias, Universidad del Bio Bio, Chillan, Chile
| | - Eugene N Muratov
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Marcus Tullius Scotti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil.
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21
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Siramshetty VB, Nguyen DT, Martinez NJ, Southall NT, Simeonov A, Zakharov AV. Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era. J Chem Inf Model 2020; 60:6007-6019. [PMID: 33259212 DOI: 10.1021/acs.jcim.0c00884] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.
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Affiliation(s)
- Vishal B Siramshetty
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Natalia J Martinez
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Noel T Southall
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States
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22
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Rodrigues GCS, Dos Santos Maia M, de Menezes RPB, Cavalcanti ABS, de Sousa NF, de Moura ÉP, Monteiro AFM, Scotti L, Scotti MT. Ligand and Structure-based Virtual Screening of Lamiaceae Diterpenes with Potential Activity against a Novel Coronavirus (2019-nCoV). Curr Top Med Chem 2020; 20:2126-2145. [PMID: 32674732 DOI: 10.2174/1568026620666200716114546] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/15/2020] [Accepted: 04/20/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND The emergence of a new coronavirus (CoV), named 2019-nCoV, as an outbreak originated in the city of Wuhan, China, has resulted in the death of more than 3,400 people this year alone and has caused worldwide an alarming situation, particularly following previous CoV epidemics, including the Severe Acute Respiratory Syndrome (SARS) in 2003 and the Middle East Respiratory Syndrome (MERS) in 2012. Currently, no exists for infections caused by CoVs; however, some natural products may represent potential treatment resources, such as those that contain diterpenes. OBJECTIVE This study aimed to use computational methods to perform a virtual screening (VS) of candidate diterpenes with the potential to act as CoV inhibitors. METHODS 1,955 diterpenes, derived from the Nepetoideae subfamily (Lamiaceae), were selected using the SistematX tool (https://sistematx.ufpb.br), which were used to make predictions. From the ChEMBL database, 3 sets of chemical structures were selected for the construction of predictive models. RESULTS The chemical structures of molecules with known activity against SARS CoV, two of which were tested for activity against specific viral proteins and one of which was tested for activity against the virus itself, were classified according to their pIC50 values [-log IC50 (mol/l)]. CONCLUSION In the consensus analysis approach, combining both ligand- and structure-based VSs, 19 compounds were selected as potential CoV inhibitors, including isotanshinone IIA (01), tanshinlactone (02), isocryptotanshinone (03), and tanshinketolactone (04), which did not present toxicity within the evaluated parameters.
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Affiliation(s)
- Gabriela Cristina Soares Rodrigues
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Mayara Dos Santos Maia
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Renata Priscila Barros de Menezes
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Natália Ferreira de Sousa
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Érika Paiva de Moura
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Alex France Messias Monteiro
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Luciana Scotti
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Marcus Tullius Scotti
- Laboratory of Cheminformatics, Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
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23
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Sosnina EA, Sosnin S, Nikitina AA, Nazarov I, Osolodkin DI, Fedorov MV. Recommender Systems in Antiviral Drug Discovery. ACS OMEGA 2020; 5:15039-15051. [PMID: 32632398 PMCID: PMC7315437 DOI: 10.1021/acsomega.0c00857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 06/03/2020] [Indexed: 06/11/2023]
Abstract
Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.
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Affiliation(s)
- Ekaterina A. Sosnina
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Institute
of Physiologically Active Compounds, RAS, Severniy pr. 1, Chernogolovka 142432, Russia
| | - Sergey Sosnin
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
| | - Anastasia A. Nikitina
- Department
of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1 bd. 3, Moscow 119991, Russia
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
| | - Ivan Nazarov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
| | - Dmitry I. Osolodkin
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
- Institute
of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Trubetskaya Ulitsa 8, Moscow 119991, Russia
| | - Maxim V. Fedorov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
- Physics
John Anderson Building, University of Strathclyde, 107 Rottenrow East, Glasgow G4 0NG, U.K.
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24
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Hemmerich J, Ecker GF. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020; 10:e1475. [PMID: 35866138 PMCID: PMC9286356 DOI: 10.1002/wcms.1475] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
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Affiliation(s)
- Jennifer Hemmerich
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
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25
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Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105777] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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26
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Cardoso-Silva J, Papageorgiou LG, Tsoka S. Network-based piecewise linear regression for QSAR modelling. J Comput Aided Mol Des 2019; 33:831-844. [PMID: 31628660 PMCID: PMC6825651 DOI: 10.1007/s10822-019-00228-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/28/2019] [Indexed: 02/07/2023]
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.
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Affiliation(s)
- Jonathan Cardoso-Silva
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London, WC1E 7JE, UK
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, UK.
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27
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Rácz A, Bajusz D, Héberger K. Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR. Mol Inform 2019; 38:e1800154. [PMID: 30945814 PMCID: PMC6767540 DOI: 10.1002/minf.201800154] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/13/2019] [Indexed: 01/03/2023]
Abstract
QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group Research Centre for Natural SciencesHungarian Academy of SciencesMagyar tudósok krt. 21117BudapestHungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group Research Centre for Natural SciencesHungarian Academy of SciencesMagyar tudósok krt. 21117BudapestHungary
| | - Károly Héberger
- Plasma Chemistry Research Group Research Centre for Natural SciencesHungarian Academy of SciencesMagyar tudósok krt. 21117BudapestHungary
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28
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Mellor C, Marchese Robinson R, Benigni R, Ebbrell D, Enoch S, Firman J, Madden J, Pawar G, Yang C, Cronin M. Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use. Regul Toxicol Pharmacol 2019; 101:121-134. [DOI: 10.1016/j.yrtph.2018.11.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 10/09/2018] [Accepted: 11/12/2018] [Indexed: 12/20/2022]
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29
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Wilm A, Kühnl J, Kirchmair J. Computational approaches for skin sensitization prediction. Crit Rev Toxicol 2018; 48:738-760. [DOI: 10.1080/10408444.2018.1528207] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Anke Wilm
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- HITeC e.V, Hamburg, Germany
| | - Jochen Kühnl
- Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- Department of Chemistry, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), University of Bergen, Bergen, Norway
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30
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Low YS, Alves VM, Fourches D, Sedykh A, Andrade CH, Muratov EN, Rusyn I, Tropsha A. Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. J Chem Inf Model 2018; 58:2203-2213. [PMID: 30376324 DOI: 10.1021/acs.jcim.8b00450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.
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Affiliation(s)
- Yen S Low
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center , North Carolina State University , Raleigh , North Carolina 27695 , United States
| | - Alexander Sedykh
- Sciome LLC , Research Triangle Park , North Carolina 27709 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa 65000 , Ukraine
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences , Texas A&M University , College Station , Texas 77843 , United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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31
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Munawar S, Windley MJ, Tse EG, Todd MH, Hill AP, Vandenberg JI, Jabeen I. Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities. Front Pharmacol 2018; 9:1035. [PMID: 30333745 PMCID: PMC6176658 DOI: 10.3389/fphar.2018.01035] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/27/2018] [Indexed: 12/17/2022] Open
Abstract
The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.
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Affiliation(s)
- Saba Munawar
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan.,Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Edwin G Tse
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Matthew H Todd
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Adam P Hill
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Ishrat Jabeen
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan
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