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1.
Pharmacol Rev ; 75(6): 1167-1199, 2023 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-37684054

RESUMO

The prokineticins (PKs) were discovered approximately 20 years ago as small peptides inducing gut contractility. Today, they are established as angiogenic, anorectic, and proinflammatory cytokines, chemokines, hormones, and neuropeptides involved in variety of physiologic and pathophysiological pathways. Their altered expression or mutations implicated in several diseases make them a potential biomarker. Their G-protein coupled receptors, PKR1 and PKR2, have divergent roles that can be therapeutic target for treatment of cardiovascular, metabolic, and neural diseases as well as pain and cancer. This article reviews and summarizes our current knowledge of PK family functions from development of heart and brain to regulation of homeostasis in health and diseases. Finally, the review summarizes the established roles of the endogenous peptides, synthetic peptides and the selective ligands of PKR1 and PKR2, and nonpeptide orthostatic and allosteric modulator of the receptors in preclinical disease models. The present review emphasizes the ambiguous aspects and gaps in our knowledge of functions of PKR ligands and elucidates future perspectives for PK research. SIGNIFICANCE STATEMENT: This review provides an in-depth view of the prokineticin family and PK receptors that can be active without their endogenous ligand and exhibits "constitutive" activity in diseases. Their non- peptide ligands display promising effects in several preclinical disease models. PKs can be the diagnostic biomarker of several diseases. A thorough understanding of the role of prokineticin family and their receptor types in health and diseases is critical to develop novel therapeutic strategies with safety concerns.


Assuntos
Neoplasias , Neuropeptídeos , Humanos , Receptores Acoplados a Proteínas G/metabolismo , Neuropeptídeos/metabolismo , Peptídeos , Neoplasias/tratamento farmacológico , Biomarcadores
2.
J Chem Inf Model ; 64(1): 42-56, 2024 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-38116926

RESUMO

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.


Assuntos
Quimioinformática , Aprendizado de Máquina
3.
Int J Mol Sci ; 23(3)2022 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-35163123

RESUMO

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.


Assuntos
Simulação por Computador , Porfirinas/química , Espectrofotometria/métodos , Modelos Moleculares , Estrutura Molecular
4.
Chem Res Toxicol ; 34(2): 541-549, 2021 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-33513003

RESUMO

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.


Assuntos
Doença Hepática Induzida por Substâncias e Drogas , Aprendizado de Máquina , Preparações Farmacêuticas/química , Bases de Dados Factuais , Humanos , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade
5.
Bioorg Chem ; 114: 105042, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34120024

RESUMO

S. aureus resistant to methicillin (MRSA) is one of the most-concerned multidrug resistant bacteria, due to its role in life-threatening infections. There is an urgent need to develop new antibiotics against MRSA. In this study, we firstly compiled a data set of 2,3-diaminoquinoxalines by chemical synthesis and antibacterial screening against S. aureus, and then performed cheminformatics modeling and virtual screening. The compound with the Specs ID of AG-205/33156020 was discovered as a new antibacterial agent, and was further identified as a Gyrase B (GyrB) inhibitor. In light of the common features, we hypothesized that the 6c as the representative of 2,3-diaminoquinoxalines also inhibited GyrB and eventually proved it. Via molecular docking and molecular dynamics simulations, we identified binding modes of AG-205/33156020 and 6c to the ATPase domain of GyrB. Importantly, these GyrB inhibitors inhibited the MRSA strains and showed selectivity to HepG2 and HUVEC. Taken together, this research work provides an effective ligand-based computational workflow for scaffold hopping in anti-MRSA drug discovery, and discovers two new GyrB inhibitors that are worthy of further development.


Assuntos
Antibacterianos/farmacologia , Staphylococcus aureus Resistente à Meticilina/efeitos dos fármacos , Quinoxalinas/farmacologia , Antibacterianos/síntese química , Antibacterianos/metabolismo , Antibacterianos/toxicidade , DNA Girase/metabolismo , Avaliação Pré-Clínica de Medicamentos , Células Hep G2 , Células Endoteliais da Veia Umbilical Humana , Humanos , Ligantes , Testes de Sensibilidade Microbiana , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Ligação Proteica , Quinoxalinas/síntese química , Quinoxalinas/metabolismo , Quinoxalinas/toxicidade , Inibidores da Topoisomerase II/síntese química , Inibidores da Topoisomerase II/metabolismo , Inibidores da Topoisomerase II/farmacologia , Inibidores da Topoisomerase II/toxicidade
6.
Chem Soc Rev ; 49(11): 3525-3564, 2020 06 07.
Artigo em Inglês | MEDLINE | ID: mdl-32356548

RESUMO

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.


Assuntos
Química Farmacêutica/métodos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Preparações Farmacêuticas/química , Algoritmos , Animais , Inteligência Artificial , Bases de Dados Factuais , Desenho de Fármacos , História do Século XX , História do Século XXI , Humanos , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade , Teoria Quântica , Reprodutibilidade dos Testes
8.
Int J Mol Sci ; 22(2)2021 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-33429999

RESUMO

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 - 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.


Assuntos
Acinetobacter baumannii/efeitos dos fármacos , Infecções Bacterianas/tratamento farmacológico , Imidazóis/química , Piridinas/química , Acinetobacter baumannii/patogenicidade , Infecções Bacterianas/microbiologia , Resistência a Múltiplos Medicamentos , Humanos , Imidazóis/síntese química , Líquidos Iônicos/síntese química , Líquidos Iônicos/química , Piridinas/síntese química , Staphylococcus aureus/efeitos dos fármacos , Staphylococcus aureus/patogenicidade , Relação Estrutura-Atividade
9.
J Comput Aided Mol Des ; 34(7): 769-782, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-31677002

RESUMO

We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC50 values than known Mdmx inhibitors. The source code of the project is available on https://github.com/bigchem/online-chem.


Assuntos
Proteínas de Ciclo Celular/antagonistas & inibidores , Desenho de Fármacos , Proteínas Proto-Oncogênicas/antagonistas & inibidores , Bibliotecas de Moléculas Pequenas , Antineoplásicos/química , Antineoplásicos/farmacologia , Sítios de Ligação , Proteínas de Ciclo Celular/química , Desenho Assistido por Computador/estatística & dados numéricos , Bases de Dados de Compostos Químicos/estatística & dados numéricos , Bases de Dados de Produtos Farmacêuticos , Descoberta de Drogas/métodos , Descoberta de Drogas/estatística & dados numéricos , Humanos , Ligantes , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Redes Neurais de Computação , Ligação Proteica , Proteínas Proto-Oncogênicas/química , Relação Quantitativa Estrutura-Atividade
10.
Chem Res Toxicol ; 37(6): 825-826, 2024 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-38769907
11.
J Chem Inf Model ; 59(3): 1062-1072, 2019 03 25.
Artigo em Inglês | MEDLINE | ID: mdl-30589269

RESUMO

Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for different species using different types of administration, etc., and it is questionable if the knowledge transfer between end points is possible. We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS). We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. Our research reveals that multitask learning can be very useful to improve the quality of acute toxicity modeling and raises a discussion about the usage of multitask approaches for regulation purposes. Our MultiTox models are freely available in OCHEM platform ( ochem.eu/multitox ) under CC-BY-NC license.


Assuntos
Aprendizado Profundo , Modelos Teóricos , Testes de Toxicidade Aguda , Animais , Determinação de Ponto Final
13.
J Chem Inf Model ; 58(5): 933-942, 2018 05 29.
Artigo em Inglês | MEDLINE | ID: mdl-29667823

RESUMO

Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set. In this study we developed models to identify these molecules using various methods, such as molecular docking, SMARTS screening, pharmacophores, and machine learning methods. Among the structure-based methods, the pharmacophore-based method showed promising results, with a balanced accuracy of 74.2%. However, machine-learning approaches using associative neural networks outperformed all of the other methods explored, producing a final model with a balanced accuracy of 89.7%. The high predictive accuracy of this model is expected to be useful for advising which compounds are potential luciferase inhibitors present in luciferase HTS assays. The models developed in this work are freely available at the OCHEM platform at http://ochem.eu .


Assuntos
Avaliação Pré-Clínica de Medicamentos/métodos , Inibidores Enzimáticos/farmacologia , Ensaios de Triagem em Larga Escala/métodos , Luciferases/antagonistas & inibidores , Inibidores Enzimáticos/química , Inibidores Enzimáticos/metabolismo , Reações Falso-Positivas , Luciferases/química , Luciferases/metabolismo , Simulação de Acoplamento Molecular , Conformação Proteica
16.
Chem Res Toxicol ; 29(5): 768-75, 2016 May 16.
Artigo em Inglês | MEDLINE | ID: mdl-27120770

RESUMO

The ToxCast EPA challenge was managed by TopCoder in Spring 2014. The goal of the challenge was to develop a model to predict the lowest effect level (LEL) concentration based on in vitro measurements and calculated in silico descriptors. This article summarizes the computational steps used to develop the Rank-I model, which calculated the lowest prediction error for the secret test data set of the challenge. The model was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM), and it is freely available at http://ochem.eu/article/68104 . Surprisingly, this model does not use any in vitro measurements. The logic of the decision steps used to develop the model and the reason to skip inclusion of in vitro measurements is described. We also show that inclusion of in vitro assays would not improve the accuracy of the model.


Assuntos
Modelos Teóricos , Relação Dose-Resposta a Droga , Técnicas In Vitro , Aprendizado de Máquina , Redes Neurais de Computação
17.
Chem Res Toxicol ; 33(3): 687-688, 2020 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-32172570
18.
J Chem Inf Model ; 60(3): 1069-1071, 2020 03 23.
Artigo em Inglês | MEDLINE | ID: mdl-32101004
19.
Molecules ; 21(1): E1, 2015 Dec 23.
Artigo em Inglês | MEDLINE | ID: mdl-26703557

RESUMO

The article describes a classification system termed "extended functional groups" (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool (http://ochem.eu/alerts) of the On-line CHEmical database and Modeling (OCHEM) environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models.


Assuntos
Bases de Dados de Compostos Químicos , Software , Relação Estrutura-Atividade
20.
J Chem Inf Model ; 54(12): 3320-9, 2014 Dec 22.
Artigo em Inglês | MEDLINE | ID: mdl-25489863

RESUMO

This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638.


Assuntos
Química Farmacêutica , Informática/métodos , Preparações Farmacêuticas/química , Temperatura de Transição , Inteligência Artificial , Modelos Estatísticos , Estatística como Assunto
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