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Le Roch M, Renault J, Argouarch G, Lenci E, Trabocchi A, Roisnel T, Gouault N, Lalli C. Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery. J Org Chem 2024; 89:4932-4946. [PMID: 38451837 DOI: 10.1021/acs.joc.4c00143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).
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Affiliation(s)
- Myriam Le Roch
- Univ Rennes, CNRS, ISCR-UMR 6226, Rennes F-35000, France
| | | | | | - Elena Lenci
- Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 13, Sesto Fiorentino, Florence 50019, Italy
| | - Andrea Trabocchi
- Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 13, Sesto Fiorentino, Florence 50019, Italy
| | - Thierry Roisnel
- Univ Rennes, Centre de Diffractométrie X (CDIFX), ISCR-UMR 6226, Rennes F-35000, France
| | | | - Claudia Lalli
- Univ Rennes, CNRS, ISCR-UMR 6226, Rennes F-35000, France
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2
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Gomatam A, Hirlekar BU, Singh KD, Murty US, Dixit VA. Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis. Mol Divers 2024:10.1007/s11030-024-10809-9. [PMID: 38374474 DOI: 10.1007/s11030-024-10809-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/07/2024] [Indexed: 02/21/2024]
Abstract
The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.
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Affiliation(s)
- Anish Gomatam
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Bhakti Umesh Hirlekar
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Krishan Dev Singh
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Upadhyayula Suryanarayana Murty
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Vaibhav A Dixit
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India.
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3
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Orsi M, Probst D, Schwaller P, Reymond JL. Alchemical analysis of FDA approved drugs. DIGITAL DISCOVERY 2023; 2:1289-1296. [PMID: 38013905 PMCID: PMC10561545 DOI: 10.1039/d3dd00039g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/29/2023] [Indexed: 11/29/2023]
Abstract
Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Daniel Probst
- Ecole Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | | | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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4
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Zhang Y, Menke J, He J, Nittinger E, Tyrchan C, Koch O, Zhao H. Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification. J Cheminform 2023; 15:75. [PMID: 37649050 PMCID: PMC10469421 DOI: 10.1186/s13321-023-00744-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 08/10/2023] [Indexed: 09/01/2023] Open
Abstract
Siamese networks, representing a novel class of neural networks, consist of two identical subnetworks sharing weights but receiving different inputs. Here we present a similarity-based pairing method for generating compound pairs to train Siamese neural networks for regression tasks. In comparison with the conventional exhaustive pairing, it reduces the algorithm complexity from O(n2) to O(n). It also results in a better prediction performance consistently on the three physicochemical datasets, using a multilayer perceptron with the circular fingerprint as a proof of concept. We further include into a Siamese neural network the transformer-based Chemformer, which extracts task-specific features from the simplified molecular-input line-entry system representation of compounds. Additionally, we propose a means to measure the prediction uncertainty by utilizing the variance in predictions from a set of reference compounds. Our results demonstrate that the high prediction accuracy correlates with the high confidence. Finally, we investigate implications of the similarity property principle in machine learning.
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Affiliation(s)
- Yumeng Zhang
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Janosch Menke
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden.
- Institute of Pharmaceutical and Medicinal Chemistry, Westfälische Wilhelms-Universität Münster, 48149, Münster, Germany.
| | - Jiazhen He
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183, Gothenburg, Sweden
| | - Eva Nittinger
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden
| | - Christian Tyrchan
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden
| | - Oliver Koch
- Institute of Pharmaceutical and Medicinal Chemistry, Westfälische Wilhelms-Universität Münster, 48149, Münster, Germany
| | - Hongtao Zhao
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183, Gothenburg, Sweden.
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5
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Spiers RC, Norby C, Kalivas JH. Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra. Anal Chem 2023; 95:12776-12784. [PMID: 37594455 DOI: 10.1021/acs.analchem.3c01616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Determining sample similarity underlies many foundational principles in analytical chemistry. For example, calibration models are unsuitable to predict outliers. Calibration transfer methods assume a moderate degree of sample and measurement dissimilarities between a calibration set and target prediction samples. Classification approaches link target sample similarities to groups of similar class samples. Although similarity is ubiquitous in analytical chemistry and everyday life, quantifying sample similarity is without a straightforward solution, especially when target domain samples are unlabeled and the only known features are measurable, such as spectra (the focus of this paper). The process proposed to assess sample similarity integrates spectral similarity information with contextual considerations among source analyte contents, model, and analyte predictions. This hybrid approach named the physicochemical responsive integrated similarity measure (PRISM) amplifies hidden-but-essential physicochemical properties encoded within respective spectra. PRISM is tested on four near-infrared (NIR) data sets for four diverse application areas to show efficacy. These applications are the assessment of prediction reliability and model updating for model generalizability, outlier detection, and basic matrix matching evaluation. Discussion is provided on adapting PRISM to classification problems. Results indicate that PRISM collects large amounts of similarity information and effectively integrates it to produce a quantitative similarity evaluation between the target sample and a source domain. The approach is also useful for biological samples with additional physiochemical variations. While PRISM is dynamically tested on NIR data, parts of PRISM were previously applied to other data types, and PRISM should be applicable to other measurement systems perturbed by matrix effects.
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Affiliation(s)
- Robert C Spiers
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
| | - Callan Norby
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
| | - John H Kalivas
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
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6
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:3906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann—La Roche Ltd., 4070 Basel, Switzerland;
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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7
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Chiodi D, Ishihara Y. "Magic Chloro": Profound Effects of the Chlorine Atom in Drug Discovery. J Med Chem 2023; 66:5305-5331. [PMID: 37014977 DOI: 10.1021/acs.jmedchem.2c02015] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Chlorine is one of the most common atoms present in small-molecule drugs beyond carbon, hydrogen, nitrogen, and oxygen. There are currently more than 250 FDA-approved chlorine-containing drugs, yet the beneficial effect of the chloro substituent has not yet been reviewed. The seemingly simple substitution of a hydrogen atom (R = H) with a chlorine atom (R = Cl) can result in remarkable improvements in potency of up to 100,000-fold and can lead to profound effects on pharmacokinetic parameters including clearance, half-life, and drug exposure in vivo. Following the literature terminology of the "magic methyl effect" in drugs, the term "magic chloro effect" has been coined herein. Although reports of 500-fold or 1000-fold potency improvements are often serendipitous discoveries that can be considered "magical" rather than planned, hypotheses made to explain the magic chloro effect can lead to lessons that accelerate the cycle of drug discovery.
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Affiliation(s)
- Debora Chiodi
- Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Yoshihiro Ishihara
- Department of Chemistry, Vividion Therapeutics, 5820 Nancy Ridge Drive, San Diego, California 92121, United States
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8
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Tysinger EP, Rai BK, Sinitskiy AV. Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models? J Chem Inf Model 2023; 63:1734-1744. [PMID: 36914216 DOI: 10.1021/acs.jcim.2c01618] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model originally developed for machine translation. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL data set, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. By retrospective analysis on the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to most active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Our work demonstrates that human experts working on hit expansion in drug design can easily and quickly employ transformer models, originally developed to translate texts from one natural language to another, to "translate" from known molecules active against a given protein target to novel molecules active against the same target.
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Affiliation(s)
- Emma P Tysinger
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Brajesh K Rai
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Anton V Sinitskiy
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
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9
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Yang ZY, Fu L, Lu AP, Liu S, Hou TJ, Cao DS. Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion. J Cheminform 2021; 13:86. [PMID: 34774096 PMCID: PMC8590336 DOI: 10.1186/s13321-021-00564-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/30/2021] [Indexed: 12/01/2022] Open
Abstract
In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline. ![]()
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China. .,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China. .,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China.
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10
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Naveja JJ, Vogt M. Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications. Molecules 2021; 26:5291. [PMID: 34500724 PMCID: PMC8433811 DOI: 10.3390/molecules26175291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 01/21/2023] Open
Abstract
Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.
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Affiliation(s)
- José J. Naveja
- Instituto de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5-6, 53115 Bonn, Germany
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11
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Tynes M, Gao W, Burrill DJ, Batista ER, Perez D, Yang P, Lubbers N. Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search. J Chem Inf Model 2021; 61:3846-3857. [PMID: 34347460 DOI: 10.1021/acs.jcim.1c00670] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning (ML) plays a growing role in the design and discovery of chemicals, aiming to reduce the need to perform expensive experiments and simulations. ML for such applications is promising but difficult, as models must generalize to vast chemical spaces from small training sets and must have reliable uncertainty quantification metrics to identify and prioritize unexplored regions. Ab initio computational chemistry and chemical intuition alike often take advantage of differences between chemical conditions, rather than their absolute structure or state, to generate more reliable results. We have developed an analogous comparison-based approach for ML regression, called pairwise difference regression (PADRE), which is applicable to arbitrary underlying learning models and operates on pairs of input data points. During training, the model learns to predict differences between all possible pairs of input points. During prediction, the test points are paired with all training set points, giving rise to a set of predictions that can be treated as a distribution of which the mean is treated as a final prediction and the dispersion is treated as an uncertainty measure. Pairwise difference regression was shown to reliably improve the performance of the random forest algorithm across five chemical ML tasks. Additionally, the pair-derived dispersion is both well correlated with model error and performs well in active learning. We also show that this method is competitive with state-of-the-art neural network techniques. Thus, pairwise difference regression is a promising tool for candidate selection algorithms used in chemical discovery.
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Affiliation(s)
- Michael Tynes
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Wenhao Gao
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Daniel J Burrill
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Enrique R Batista
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Danny Perez
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ping Yang
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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12
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Shan J, Ji C. MolOpt: A Web Server for Drug Design using Bioisosteric Transformation. Curr Comput Aided Drug Des 2021; 16:460-466. [PMID: 31272357 DOI: 10.2174/1573409915666190704093400] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/12/2019] [Accepted: 06/13/2019] [Indexed: 01/03/2023]
Abstract
BACKGROUND Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task. METHODS In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next. RESULTS AND DISCUSSION By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study. CONCLUSION MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.
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Affiliation(s)
- Jinwen Shan
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Changge Ji
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
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13
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Awale M, Hert J, Guasch L, Riniker S, Kramer C. The Playbooks of Medicinal Chemistry Design Moves. J Chem Inf Model 2021; 61:729-742. [PMID: 33522806 DOI: 10.1021/acs.jcim.0c01143] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting "playbooks of medicinal chemistry design moves" capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.
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Affiliation(s)
- Mahendra Awale
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Jérôme Hert
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Laura Guasch
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Christian Kramer
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
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14
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Ichikawa Y, Hiramatsu M, Mita Y, Makishima M, Matsumoto Y, Masumoto Y, Muranaka A, Uchiyama M, Hashimoto Y, Ishikawa M. meta-Non-flat substituents: a novel molecular design to improve aqueous solubility in small molecule drug discovery. Org Biomol Chem 2021; 19:446-456. [PMID: 33331380 DOI: 10.1039/d0ob02083d] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Aqueous solubility is a key requirement for small-molecule drug candidates. Here, we investigated the regioisomer-physicochemical property relationships of disubstituted benzenes. We found that meta-isomers bearing non-flat substituents tend to possess the lowest melting point and the highest thermodynamic aqueous solubility among the regioisomers. The examination of pharmaceutical compounds containing a disubstituted benzene moiety supported the idea that the introduction of a non-flat substituent at the meta position of a benzene substructure would be a promising approach for medicinal chemists aiming to improve the thermodynamic aqueous solubility of drug candidates, even though it might not be universally effective.
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Affiliation(s)
- Yuki Ichikawa
- Institute for Quantitative Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Michiaki Hiramatsu
- Institute for Quantitative Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Yusuke Mita
- Institute for Quantitative Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Makoto Makishima
- Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo 173-8610, Japan
| | - Yotaro Matsumoto
- Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3, Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Yui Masumoto
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Atsuya Muranaka
- Advanced Elements Chemistry Laboratory, RIKEN Cluster for Pioneering Research (CPR), 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Masanobu Uchiyama
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Advanced Elements Chemistry Laboratory, RIKEN Cluster for Pioneering Research (CPR), 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Yuichi Hashimoto
- Institute for Quantitative Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Minoru Ishikawa
- Graduate School of Life Sciences, Tohoku University, 2-1-1, Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan.
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15
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Fu L, Yang ZY, Yang ZJ, Yin MZ, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. QSAR-assisted-MMPA to expand chemical transformation space for lead optimization. Brief Bioinform 2021; 22:6071857. [PMID: 33418563 DOI: 10.1093/bib/bbaa374] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/25/2020] [Accepted: 11/25/2020] [Indexed: 11/13/2022] Open
Abstract
Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.
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Affiliation(s)
- Li Fu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China.,Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ming-Zhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R China
| | - Xiang Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China.,Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China.,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R China
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16
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Awale M, Riniker S, Kramer C. Matched Molecular Series Analysis for ADME Property Prediction. J Chem Inf Model 2020; 60:2903-2914. [PMID: 32369360 DOI: 10.1021/acs.jcim.0c00269] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Generation and prioritization of new molecules are the most central part of the drug design process. Matched molecular series analysis (MMSA) has recently been proposed as a formal approach that captures both of these key elements of design. In order to better understand the power of MMSA and its specific limitations, we here evaluate its performance as an ADME property prediction tool. We use four large and diverse inhouse data sets, logD, microsomal clearance, CYP2C9, and CYP3A4 inhibition. MMSA follows the concept of parallel structure-activity relationship (SAR), where if two identical substituent series on different scaffolds show similarity in their property profiles, SAR from one series can be transferred to the other series. We test four different similarity metrics to identify pairs of molecular series where information can be transferred. We find that the best prediction performance is achieved by a combination of centered root-mean-square deviation (cRMSD) and a network score approach previously published by Keefer et al. However, cRMSD alone strikes the best balance between accuracy and the number of predictions that can be made. We identify statistical metrics that allow estimating when MMSA predictions will work, similar to the well-known applicability domain concept in machine learning. MMSA achieves a prediction accuracy that is comparable to a standard machine-learning model and matched molecular pair analysis. In contrast to machine learning, however, it is very easy to understand where MMSA predictions are coming from. Finally, to prospectively test the power of MMSA, we retested compounds that were strong outliers in the initial predictions and show how the MMSA model can help to identify erroneous data points.
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Affiliation(s)
- Mahendra Awale
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Christian Kramer
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
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17
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Garrido A, Lepailleur A, Mignani SM, Dallemagne P, Rochais C. hERG toxicity assessment: Useful guidelines for drug design. Eur J Med Chem 2020; 195:112290. [PMID: 32283295 DOI: 10.1016/j.ejmech.2020.112290] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/27/2020] [Accepted: 03/27/2020] [Indexed: 02/06/2023]
Abstract
All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.
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Affiliation(s)
- Amanda Garrido
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Alban Lepailleur
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Serge M Mignani
- UMR 860, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologique, Université Paris Descartes, PRES Sorbonne Paris Cité, CNRS, 45 rue des Saints Pères, 75006, Paris, France; CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, 9020-105, Funchal, Portugal
| | - Patrick Dallemagne
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France
| | - Christophe Rochais
- Normandie Univ, UNICAEN, Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Caen, France.
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18
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Ambure P, Cordeiro MNDS. Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2020. [DOI: 10.1007/978-1-0716-0150-1_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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19
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Fu L, Liu L, Yang ZJ, Li P, Ding JJ, Yun YH, Lu AP, Hou TJ, Cao DS. Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis. J Chem Inf Model 2019; 60:63-76. [DOI: 10.1021/acs.jcim.9b00718] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Lu Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Pan Li
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
| | - Jun-Jie Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
| | - Yong-Huan Yun
- College of Food Science and Engineering, Hainan University, Haikou 570228, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, P. R. China
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20
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Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence? Drug Discov Today 2018; 23:1373-1384. [DOI: 10.1016/j.drudis.2018.03.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 02/27/2018] [Accepted: 03/20/2018] [Indexed: 12/18/2022]
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21
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Dalke A, Hert J, Kramer C. mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets. J Chem Inf Model 2018; 58:902-910. [DOI: 10.1021/acs.jcim.8b00173] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrew Dalke
- Andrew Dalke Scientific AB, SE-461 30 Trollhättan, Sweden
| | - Jérôme Hert
- Roche Pharma Research and Early Development, Roche Innovation Center, CH-4070 Basel, Switzerland
| | - Christian Kramer
- Roche Pharma Research and Early Development, Roche Innovation Center, CH-4070 Basel, Switzerland
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22
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Sato T, Hashimoto N, Honma T. Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments. J Chem Inf Model 2017; 57:2938-2947. [DOI: 10.1021/acs.jcim.7b00092] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tomohiro Sato
- RIKEN Center for Life Science
Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Noriaki Hashimoto
- Watarase Research Center, Kyorin Pharmaceutical Co.,
Ltd., 1848 Nogi, Nogi-machi, Shimotsuga-gun, Tochigi 329-0114, Japan
| | - Teruki Honma
- RIKEN Center for Life Science
Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
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23
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Lukac I, Zarnecka J, Griffen EJ, Dossetter AG, St-Gallay SA, Enoch SJ, Madden JC, Leach AG. Turbocharging Matched Molecular Pair Analysis: Optimizing the Identification and Analysis of Pairs. J Chem Inf Model 2017; 57:2424-2436. [DOI: 10.1021/acs.jcim.7b00335] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Iva Lukac
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Joanna Zarnecka
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | | | | | | | - Steven J. Enoch
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Judith C. Madden
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
| | - Andrew G. Leach
- School
of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, U.K
- MedChemica Ltd., BioHub, Alderley
Park, Macclesfield SK10
4TG, U.K
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24
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Kramer C, Ting A, Zheng H, Hert J, Schindler T, Stahl M, Robb G, Crawford JJ, Blaney J, Montague S, Leach AG, Dossetter AG, Griffen EJ. Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA). J Med Chem 2017; 61:3277-3292. [DOI: 10.1021/acs.jmedchem.7b00935] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Christian Kramer
- Roche Pharma Research and Early Development, Roche Innovation
Center, Basel CH-4070, Switzerland
| | - Attilla Ting
- AstraZeneca PLC, Milton Road, Cambridge CB4 0FZ, U.K
| | - Hao Zheng
- Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Jérôme Hert
- Roche Pharma Research and Early Development, Roche Innovation
Center, Basel CH-4070, Switzerland
| | - Torsten Schindler
- Roche Pharma Research and Early Development, Roche Innovation
Center, Basel CH-4070, Switzerland
| | - Martin Stahl
- Roche Pharma Research and Early Development, Roche Innovation
Center, Basel CH-4070, Switzerland
| | - Graeme Robb
- AstraZeneca PLC, Milton Road, Cambridge CB4 0FZ, U.K
| | - James J. Crawford
- Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Jeff Blaney
- Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Shane Montague
- MedChemica Ltd., Biohub Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K
| | - Andrew G. Leach
- MedChemica Ltd., Biohub Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K
| | - Al G. Dossetter
- MedChemica Ltd., Biohub Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K
| | - Ed J. Griffen
- MedChemica Ltd., Biohub Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K
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25
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Lombardo F, Desai PV, Arimoto R, Desino KE, Fischer H, Keefer CE, Petersson C, Winiwarter S, Broccatelli F. In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development. J Med Chem 2017; 60:9097-9113. [DOI: 10.1021/acs.jmedchem.7b00487] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Franco Lombardo
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Prashant V. Desai
- Computational
ADME, Drug Disposition, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Rieko Arimoto
- Vertex Pharmaceuticals Inc., 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | | | - Holger Fischer
- Roche
Pharmaceutical Research and Early Development, Pharmaceutical Sciences,
Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | | | - Carl Petersson
- Discovery Drug Disposition, Biopharma, R&D Global Early Development, EMD Serono, Frankfurter Strasse 250 I Postcode D39/001, 64293 Darmstadt, Germany
| | - Susanne Winiwarter
- Drug Safety and Metabolism, AstraZeneca R&D Gothenburg, 431 83 Mölndal, Sweden
| | - Fabio Broccatelli
- Genentech Inc., South San Francisco, California 94080, United States
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26
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Gomes MN, Braga RC, Grzelak EM, Neves BJ, Muratov E, Ma R, Klein LL, Cho S, Oliveira GR, Franzblau SG, Andrade CH. QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity. Eur J Med Chem 2017; 137:126-138. [PMID: 28582669 DOI: 10.1016/j.ejmech.2017.05.026] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/04/2017] [Accepted: 05/08/2017] [Indexed: 10/19/2022]
Abstract
New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents.
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Affiliation(s)
- Marcelo N Gomes
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil
| | - Rodolpho C Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil
| | - Edyta M Grzelak
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Bruno J Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil; Postgraduate Program of Society, Technology and Environment, University Center of Anápolis/UniEVANGELICA, Anápolis, Goiás, 75083-515, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27955-7568, United States; Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Rui Ma
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Larry L Klein
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Sanghyun Cho
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | | | - Scott G Franzblau
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States.
| | - Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil.
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27
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Yeung KS, Beno BR, Parcella K, Bender JA, Grant-Young KA, Nickel A, Gunaga P, Anjanappa P, Bora RO, Selvakumar K, Rigat K, Wang YK, Liu M, Lemm J, Mosure K, Sheriff S, Wan C, Witmer M, Kish K, Hanumegowda U, Zhuo X, Shu YZ, Parker D, Haskell R, Ng A, Gao Q, Colston E, Raybon J, Grasela DM, Santone K, Gao M, Meanwell NA, Sinz M, Soars MG, Knipe JO, Roberts SB, Kadow JF. Discovery of a Hepatitis C Virus NS5B Replicase Palm Site Allosteric Inhibitor (BMS-929075) Advanced to Phase 1 Clinical Studies. J Med Chem 2017; 60:4369-4385. [PMID: 28430437 DOI: 10.1021/acs.jmedchem.7b00328] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The hepatitis C virus (HCV) NS5B replicase is a prime target for the development of direct-acting antiviral drugs for the treatment of chronic HCV infection. Inspired by the overlay of bound structures of three structurally distinct NS5B palm site allosteric inhibitors, the high-throughput screening hit anthranilic acid 4, the known benzofuran analogue 5, and the benzothiadiazine derivative 6, an optimization process utilizing the simple benzofuran template 7 as a starting point for a fragment growing approach was pursued. A delicate balance of molecular properties achieved via disciplined lipophilicity changes was essential to achieve both high affinity binding and a stringent targeted absorption, distribution, metabolism, and excretion profile. These efforts led to the discovery of BMS-929075 (37), which maintained ligand efficiency relative to early leads, demonstrated efficacy in a triple combination regimen in HCV replicon cells, and exhibited consistently high oral bioavailability and pharmacokinetic parameters across preclinical animal species. The human PK properties from the Phase I clinical studies of 37 were better than anticipated and suggest promising potential for QD administration.
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Affiliation(s)
- Kap-Sun Yeung
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Brett R Beno
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Kyle Parcella
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - John A Bender
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Katherine A Grant-Young
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Andrew Nickel
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Prashantha Gunaga
- Department of Discovery Chemistry, Biocon Bristol-Myers Squibb Research and Development Center , Biocon Park, Jigani Link Road, Bommasandra IV, Bangalore 560099, India
| | - Prakash Anjanappa
- Department of Discovery Chemistry, Biocon Bristol-Myers Squibb Research and Development Center , Biocon Park, Jigani Link Road, Bommasandra IV, Bangalore 560099, India
| | - Rajesh Onkardas Bora
- Department of Discovery Chemistry, Biocon Bristol-Myers Squibb Research and Development Center , Biocon Park, Jigani Link Road, Bommasandra IV, Bangalore 560099, India
| | - Kumaravel Selvakumar
- Department of Discovery Chemistry, Biocon Bristol-Myers Squibb Research and Development Center , Biocon Park, Jigani Link Road, Bommasandra IV, Bangalore 560099, India
| | - Karen Rigat
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Ying-Kai Wang
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Mengping Liu
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Julie Lemm
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Kathy Mosure
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Steven Sheriff
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Changhong Wan
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Mark Witmer
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Kevin Kish
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Umesh Hanumegowda
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Xiaoliang Zhuo
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Yue-Zhong Shu
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Dawn Parker
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Roy Haskell
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Alicia Ng
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Qi Gao
- Bristol-Myers Squibb Research and Development , 1 Squibb Drive, New Brunswick, New Jersey 08901, United States
| | - Elizabeth Colston
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Joseph Raybon
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Dennis M Grasela
- Bristol-Myers Squibb Research and Development , P.O. Box 4000, Princeton, New Jersey 08543, United States
| | - Kenneth Santone
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Min Gao
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Nicholas A Meanwell
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Michael Sinz
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Matthew G Soars
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Jay O Knipe
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - Susan B Roberts
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
| | - John F Kadow
- Bristol-Myers Squibb Research and Development , P.O. Box 5100, 5 Research Parkway, Wallingford, Connecticut 06492, United States
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28
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Affiliation(s)
- Emanuel S. R. Ehmki
- Chemical Biology/Therapeutic
Modalities, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Grenzacherstrasse
124, 4070 Basel, Switzerland
| | - Christian Kramer
- Chemical Biology/Therapeutic
Modalities, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Grenzacherstrasse
124, 4070 Basel, Switzerland
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29
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Pennington LD, Moustakas DT. The Necessary Nitrogen Atom: A Versatile High-Impact Design Element for Multiparameter Optimization. J Med Chem 2017; 60:3552-3579. [PMID: 28177632 DOI: 10.1021/acs.jmedchem.6b01807] [Citation(s) in RCA: 183] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
There is a continued desire in biomedical research to reduce the number and duration of design cycles required to optimize lead compounds into high-quality chemical probes or safe and efficacious drug candidates. The insightful application of impactful molecular design elements is one approach toward achieving this goal. The replacement of a CH group with a N atom in aromatic and heteroaromatic ring systems can have many important effects on molecular and physicochemical properties and intra- and intermolecular interactions that can translate to improved pharmacological profiles. In this Perspective, the "necessary nitrogen atom" is shown to be a versatile high-impact design element for multiparameter optimization, wherein ≥10-, 100-, or 1000-fold improvement in a variety of key pharmacological parameters can be realized.
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Affiliation(s)
- Lewis D Pennington
- Medicinal Chemistry and ‡Modeling and Informatics, Alkermes, Plc , 852 Winter Street, Waltham, Massachusetts 02451-1420, United States
| | - Demetri T Moustakas
- Medicinal Chemistry and ‡Modeling and Informatics, Alkermes, Plc , 852 Winter Street, Waltham, Massachusetts 02451-1420, United States
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30
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Borges NM, Kenny PW, Montanari CA, Prokopczyk IM, Ribeiro JFR, Rocha JR, Sartori GR. The influence of hydrogen bonding on partition coefficients. J Comput Aided Mol Des 2017; 31:163-181. [PMID: 28054187 DOI: 10.1007/s10822-016-0002-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/16/2016] [Indexed: 11/28/2022]
Abstract
This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect 'frustration' of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.
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Affiliation(s)
- Nádia Melo Borges
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Peter W Kenny
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil.
| | - Carlos A Montanari
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Igor M Prokopczyk
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Jean F R Ribeiro
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Josmar R Rocha
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
| | - Geraldo Rodrigues Sartori
- Grupo de Estudos em Química Medicinal - NEQUIMED, Instituto de Química de São Carlos - Universidade de São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil
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31
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Chang G, Huard K, Kauffman GW, Stepan AF, Keefer CE. A multi-endpoint matched molecular pair (MMP) analysis of 6-membered heterocycles. Bioorg Med Chem 2017; 25:381-388. [DOI: 10.1016/j.bmc.2016.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 10/28/2016] [Accepted: 11/01/2016] [Indexed: 11/28/2022]
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32
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Danielson ML, Hu B, Shen J, Desai PV. In Silico ADME Techniques Used in Early-Phase Drug Discovery. TRANSLATING MOLECULES INTO MEDICINES 2017. [DOI: 10.1007/978-3-319-50042-3_4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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33
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Tyrchan C, Evertsson E. Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations. Comput Struct Biotechnol J 2016; 15:86-90. [PMID: 28066532 PMCID: PMC5198793 DOI: 10.1016/j.csbj.2016.12.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 12/08/2016] [Accepted: 12/09/2016] [Indexed: 12/02/2022] Open
Abstract
Molecular matched pair (MMP) analysis has been used for more than 40 years within molecular design and is still an important tool to analyse potency data and other compound properties. The methods used to find matched pairs range from manual inspection, through supervised methods to unsupervised methods, which are able to find previously unknown molecular pairs. Recent publications demonstrate the value of automatic MMP analysis of publicly available bioactivity databases. The MMP concept has its limitations, but because of its easy to use and intuitive nature, it will remain one of the most important tools in the toolbox of many drug designers.
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34
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Didziapetris R, Lanevskij K. Compilation and physicochemical classification analysis of a diverse hERG inhibition database. J Comput Aided Mol Des 2016; 30:1175-1188. [DOI: 10.1007/s10822-016-9986-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Accepted: 10/21/2016] [Indexed: 10/20/2022]
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35
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Ritchie TJ, Macdonald SJF. Heterocyclic replacements for benzene: Maximising ADME benefits by considering individual ring isomers. Eur J Med Chem 2016; 124:1057-1068. [PMID: 27783976 DOI: 10.1016/j.ejmech.2016.10.029] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 10/11/2016] [Accepted: 10/14/2016] [Indexed: 11/29/2022]
Abstract
The impact of replacing a mono-substituted benzene (phenyl) ring with thirty three aromatic and nine aliphatic heterocycles on nine ADME-related screens (solubility, lipophilicity, permeability, protein binding CYP450 inhibition and metabolic clearance) was assessed using matched molecular pair analysis. The results indicate that the influence on the ADME profile can differ significantly depending on the ring identity and importantly on the individual regioisomers that are possible for some rings. This information enables the medicinal chemist to make an informed choice about which rings and regioisomers to employ as mono-substituted benzene replacements, based upon the knowledge of how such replacements are likely to influence ADME-related parameters, for example to target higher solubility whilst avoiding CYP450 liabilities.
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Affiliation(s)
| | - Simon J F Macdonald
- Fibrosis Discovery Performance Unit, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK.
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36
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Polishchuk P, Tinkov O, Khristova T, Ognichenko L, Kosinskaya A, Varnek A, Kuz’min V. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis. J Chem Inf Model 2016; 56:1455-69. [DOI: 10.1021/acs.jcim.6b00371] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute
of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Oleg Tinkov
- T. G. Shevchenko Transdniestria State University, ul. 25 Oktyabrya 107, 3300 Tiraspol, Transdniestria, Republic of Moldova
| | - Tatiana Khristova
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
- Laboratoire
de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 1 rue Blaise Pascal, 67000 Strasbourg, France
| | - Ludmila Ognichenko
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Anna Kosinskaya
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
| | - Alexandre Varnek
- Laboratoire
de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 1 rue Blaise Pascal, 67000 Strasbourg, France
- Laboratory
of Chemoinformatics and Molecular Modeling, Butlerov Institut of Chemistry, Kazan Federal University, Kremlevskaya 18, Kazan, Russia
| | - Victor Kuz’min
- A. V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya
doroga 86, 65080 Odessa, Ukraine
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37
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Hao M, Bryant SH, Wang Y. Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem. J Cheminform 2016; 8:37. [PMID: 27398098 PMCID: PMC4938998 DOI: 10.1186/s13321-016-0150-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 07/01/2016] [Indexed: 01/17/2023] Open
Abstract
Background As one of the largest publicly accessible databases for hosting chemical structures and biological activities, PubChem has been processing bioassay submissions from the community since 2004. With the increase in volume for the deposited data in PubChem, the diversity and wealth of information content also grows. Recently, the Tox21 program, has deposited a series of pairwise data in PubChem regarding to different mechanism of actions (MOA), such as androgen receptor (AR) agonist and antagonist datasets, to study cell toxicity. To the best of our knowledge, little work has been reported from cheminformatics study for these especially pairwise datasets, which may provide insight into the mechanism of actions of the compounds and relationship between chemical structures and functions, as well as guidance for lead compound selection and optimization. Thus, to fill the gap, we performed a comprehensive cheminformatics analysis, including scaffold analysis, matched molecular pair (MMP) analysis as well as activity cliff analysis to investigate the structural characteristics and discontinued structure–activity relationship of the individual dataset (i.e., AR agonist dataset or AR antagonist dataset) and the combined dataset (i.e., the common compounds between the AR agonist and antagonist datasets). Results Scaffolds associated only with potential agonists or antagonists were identified. MMP-based activity cliffs, as well as a small group of compounds with dual MOA reported were recognized and analyzed. Moreover, MOA-cliff, a novel concept, was proposed to indicate one pair of structurally similar molecules which exhibit opposite MOA. Conclusions Cheminformatics methods were successfully applied to the pairwise AR datasets and the identified molecular scaffold characteristics, MMPs as well as activity cliffs might provide useful information when designing new lead compounds for the androgen receptor. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0150-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
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38
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Loeffler JR, Ehmki ESR, Fuchs JE, Liedl KR. Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design. J Comput Aided Mol Des 2016; 30:391-400. [PMID: 27272323 PMCID: PMC4912590 DOI: 10.1007/s10822-016-9913-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 05/02/2016] [Indexed: 11/25/2022]
Abstract
Urea derivatives are ubiquitously found in many chemical disciplines. N,N'-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.
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Affiliation(s)
- Johannes R Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
| | - Emanuel S R Ehmki
- Institute of General, Inorganic and Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
| | - Julian E Fuchs
- Institute of General, Inorganic and Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria.
| | - Klaus R Liedl
- Institute of General, Inorganic and Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
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39
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Giroud M, Harder M, Kuhn B, Haap W, Trapp N, Schweizer WB, Schirmeister T, Diederich F. Fluorine Scan of Inhibitors of the Cysteine Protease Human Cathepsin L: Dipolar and Quadrupolar Effects in the π-Stacking of Fluorinated Phenyl Rings on Peptide Amide Bonds. ChemMedChem 2016; 11:1042-7. [DOI: 10.1002/cmdc.201600132] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 03/23/2016] [Indexed: 11/10/2022]
Affiliation(s)
- Maude Giroud
- Laboratorium für Organische Chemie; ETH Zürich; Wolfgang-Pauli-Strasse 10, HCI 8093 Zürich Switzerland
| | - Michael Harder
- Laboratorium für Organische Chemie; ETH Zürich; Wolfgang-Pauli-Strasse 10, HCI 8093 Zürich Switzerland
| | - Bernd Kuhn
- Small Molecule Research; Roche Innovation Center Basel; F. Hoffmann-La Roche AG; Grenzacherstrasse 124, Building 92 4070 Basel Switzerland
| | - Wolfgang Haap
- Small Molecule Research; Roche Innovation Center Basel; F. Hoffmann-La Roche AG; Grenzacherstrasse 124, Building 92 4070 Basel Switzerland
| | - Nils Trapp
- Laboratorium für Organische Chemie; ETH Zürich; Wolfgang-Pauli-Strasse 10, HCI 8093 Zürich Switzerland
| | - W. Bernd Schweizer
- Laboratorium für Organische Chemie; ETH Zürich; Wolfgang-Pauli-Strasse 10, HCI 8093 Zürich Switzerland
| | - Tanja Schirmeister
- Institut für Pharmazie und Biochemie; Johannes Gutenberg-Universität Mainz; Staudinger Weg 5 55128 Mainz Germany
| | - François Diederich
- Laboratorium für Organische Chemie; ETH Zürich; Wolfgang-Pauli-Strasse 10, HCI 8093 Zürich Switzerland
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40
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Cortes-Ciriano I. Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets. J Cheminform 2016; 8:13. [PMID: 26949417 PMCID: PMC4779235 DOI: 10.1186/s13321-016-0125-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 02/22/2016] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Assessing compound toxicity at early stages of the drug discovery process is a crucial task to dismiss drug candidates likely to fail in clinical trials. Screening drug candidates against structural alerts, i.e. chemical fragments associated to a toxicological response prior or after being metabolized (bioactivation), has proved a valuable approach for this task. During the last decades, diverse algorithms have been proposed for the automatic derivation of structural alerts from categorical toxicity data sets. RESULTS AND CONCLUSIONS Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e.g. toxic/non-toxic, and continuous bioactivity data sets, e.g. [Formula: see text] or [Formula: see text] values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.
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Affiliation(s)
- Isidro Cortes-Ciriano
- Unité de Bioinformatique Structurale, CNRS UMR 3825, Département de Biologie Structurale et Chimie, Institut Pasteur, 25, rue du Dr. Roux, 75015 Paris, France
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41
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Baburajeev CP, Mohan CD, Patil GS, Rangappa S, Pandey V, Sebastian A, Fuchs JE, Bender A, Lobie PE, Basappa B, Rangappa KS. Nano-cuprous oxide catalyzed one-pot synthesis of a carbazole-based STAT3 inhibitor: a facile approach via intramolecular C–N bond formation reactions. RSC Adv 2016. [DOI: 10.1039/c6ra01906d] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In this study, we report the one-pot synthesis of substituted carbazole derivatives using nano cuprous oxide as a catalyst and demonstrated the STAT3 inhibitory activity of new compounds.
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Affiliation(s)
- C. P. Baburajeev
- Laboratory of Chemical Biology
- Department of Chemistry
- Bangalore University
- Bangalore 560001
- India
| | | | | | - Shobith Rangappa
- Frontier Research Center for Post-Genome Science and Technology
- Hokkaido University
- Sapporo 060-0808
- Japan
| | - Vijay Pandey
- Cancer Science Institute of Singapore
- National University of Singapore
- Singapore 117599
| | - Anusha Sebastian
- Laboratory of Chemical Biology
- Department of Chemistry
- Bangalore University
- Bangalore 560001
- India
| | - Julian E. Fuchs
- Centre for Molecular Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge
- UK
| | - Andreas Bender
- Centre for Molecular Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge
- UK
| | - Peter E. Lobie
- Cancer Science Institute of Singapore
- National University of Singapore
- Singapore 117599
| | - Basappa Basappa
- Laboratory of Chemical Biology
- Department of Chemistry
- Bangalore University
- Bangalore 560001
- India
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42
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Fuchs JE, Wellenzohn B, Weskamp N, Liedl KR. Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications. J Chem Inf Model 2015; 55:2315-23. [PMID: 26501781 PMCID: PMC4658635 DOI: 10.1021/acs.jcim.5b00476] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Biopharmaceuticals hold great promise
for the future of drug discovery.
Nevertheless, rational drug design strategies are mainly focused on
the discovery of small synthetic molecules. Herein we present matched
peptides, an innovative analysis technique for biological data related
to peptide and protein sequences. It represents an extension of matched
molecular pair analysis toward macromolecular sequence data and allows
quantitative predictions of the effect of single amino acid substitutions
on the basis of statistical data on known transformations. We demonstrate
the application of matched peptides to a data set of major histocompatibility
complex class II peptide ligands and discuss the trends captured with
respect to classical quantitative structure–activity relationship
approaches as well as structural aspects of the investigated protein–peptide
interface. We expect our novel readily interpretable tool at the interface
of cheminformatics and bioinformatics to support the rational design
of biopharmaceuticals and give directions for further development
of the presented methodology.
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Affiliation(s)
- Julian E Fuchs
- Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck , Innrain 82, 6020 Innsbruck, Austria
| | - Bernd Wellenzohn
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Nils Weskamp
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Klaus R Liedl
- Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck , Innrain 82, 6020 Innsbruck, Austria
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43
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The impact of data integrity on decision making in early lead discovery. J Comput Aided Mol Des 2015; 29:911-21. [DOI: 10.1007/s10822-015-9871-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 09/19/2015] [Indexed: 10/23/2022]
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44
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Abstract
Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.
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Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, Bonn, D-53113, Germany
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45
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Kramer C, Fuchs JE, Liedl KR. Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts. J Chem Inf Model 2015; 55:483-94. [PMID: 25760829 PMCID: PMC4372821 DOI: 10.1021/acs.jcim.5b00018] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Nonadditivity
in protein–ligand affinity data represents
highly instructive structure–activity relationship (SAR) features
that indicate structural changes and have the potential to guide rational
drug design. At the same time, nonadditivity is a challenge for both
basic SAR analysis as well as many ligand-based data analysis techniques
such as Free-Wilson Analysis and Matched Molecular Pair analysis,
since linear substituent contribution models inherently assume additivity
and thus do not work in such cases. While structural causes for nonadditivity
have been analyzed anecdotally, no systematic approaches to interpret
and use nonadditivity prospectively have been developed yet. In this
contribution, we lay the statistical framework for systematic analysis
of nonadditivity in a SAR series. First, we develop a general metric
to quantify nonadditivity. Then, we demonstrate the non-negligible
impact of experimental uncertainty that creates apparent nonadditivity,
and we introduce techniques to handle experimental uncertainty. Finally,
we analyze public SAR data sets for strong nonadditivity and use recourse
to the original publications and available X-ray structures to find
structural explanations for the nonadditivity observed. We find that
all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units)
with sufficient structural information to generate reasonable hypothesis
involve changes in binding mode. With the appropriate statistical
basis, nonadditivity analysis offers a variety of new attempts for
various areas in computer-aided drug design, including the validation
of scoring functions and free energy perturbation approaches, binding
pocket classification, and novel features in SAR analysis tools.
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Affiliation(s)
- Christian Kramer
- †Department of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julian E Fuchs
- †Department of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria.,‡Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Klaus R Liedl
- †Department of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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46
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Brown DG, Gagnon MM, Boström J. Understanding Our Love Affair with p-Chlorophenyl: Present Day Implications from Historical Biases of Reagent Selection. J Med Chem 2015; 58:2390-405. [DOI: 10.1021/jm501894t] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Dean G. Brown
- Infection Innovative Medicines, AstraZeneca R&D Boston, Waltham, Massachusetts 02451, United States
| | - Moriah M. Gagnon
- Infection Innovative Medicines, AstraZeneca R&D Boston, Waltham, Massachusetts 02451, United States
| | - Jonas Boström
- CVMD
Innovative Medicines, AstraZeneca, Mölndal SE-431
83, Sweden
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47
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Cortes-Ciriano I, Murrell DS, van Westen GJ, Bender A, Malliavin TE. Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling. J Cheminform 2015; 7:1. [PMID: 25705261 PMCID: PMC4335128 DOI: 10.1186/s13321-014-0049-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 11/21/2014] [Indexed: 12/16/2022] Open
Abstract
Cyclooxygenases (COX) are present in the body in two isoforms, namely: COX-1, constitutively expressed, and COX-2, induced in physiopathological conditions such as cancer or chronic inflammation. The inhibition of COX with non-steroideal anti-inflammatory drugs (NSAIDs) is the most widely used treatment for chronic inflammation despite the adverse effects associated to prolonged NSAIDs intake. Although selective COX-2 inhibition has been shown not to palliate all adverse effects (e.g. cardiotoxicity), there are still niche populations which can benefit from selective COX-2 inhibition. Thus, capitalizing on bioactivity data from both isoforms simultaneously would contribute to develop COX inhibitors with better safety profiles. We applied ensemble proteochemometric modeling (PCM) for the prediction of the potency of 3,228 distinct COX inhibitors on 11 mammalian cyclooxygenases. Ensemble PCM models ([Formula: see text], and RMSEtest = 0.71) outperformed models exclusively trained on compound ([Formula: see text], and RMSEtest = 1.09) or protein descriptors ([Formula: see text] and RMSEtest = 1.10) on the test set. Moreover, PCM predicted COX potency for 1,086 selective and non-selective COX inhibitors with [Formula: see text] and RMSEtest = 0.76. These values are in agreement with the maximum and minimum achievable [Formula: see text] and RMSEtest values of approximately 0.68 for both metrics. Confidence intervals for individual predictions were calculated from the standard deviation of the predictions from the individual models composing the ensembles. Finally, two substructure analysis pipelines singled out chemical substructures implicated in both potency and selectivity in agreement with the literature. Graphical AbstractPrediction of uncorrelated bioactivity profiles for mammalian COX inhibitors with Ensemble Proteochemometric Modeling.
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Affiliation(s)
- Isidro Cortes-Ciriano
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825, 25, rue du Dr Roux, Paris, 75015 France
| | - Daniel S Murrell
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Gerard Jp van Westen
- European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Andreas Bender
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Thérèse E Malliavin
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825, 25, rue du Dr Roux, Paris, 75015 France
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48
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Ritchie TJ, Macdonald SJF, Pickett SD. Insights into the impact of N- and O-methylation on aqueous solubility and lipophilicity using matched molecular pair analysis. MEDCHEMCOMM 2015. [DOI: 10.1039/c5md00309a] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The impact of N- and O-methylation on aqueous solubility and measured lipophilicity for several chemically diverse structural classes is described.
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Affiliation(s)
| | - S. J. F. Macdonald
- Fibrosis Discovery Performance Unit
- GlaxoSmithKline Medicines Research Centre
- Stevenage SG1 2NY
- UK
| | - S. D. Pickett
- Computational and Structural Chemistry
- GlaxoSmithKline Medicines Research Centre
- Stevenage SG1 2NY
- UK
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49
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A Mini-review on Chemoinformatics Approaches for Drug Discovery. JOURNAL OF COMPUTER AIDED CHEMISTRY 2015. [DOI: 10.2751/jcac.16.15] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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