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Interrogating Novel Areas of Chemical Space for Drug Discovery using chemoinformatics

DRUG DEVELOPMENT RESEARCH 2012年第7期73卷 430-438页

作者： Medina-Franco, Jose L. Torrey Pines Inst Mol Studies Port St Lucie FL 34987 USA

Preclinical Research Chemoinformatic approaches have an essential role in the systematic description and visualization of the chemical space for drug discovery projects. These methods enable the quantitative comparison of general screening collections and the systematic classification of approved drugs and databases annotated with biological activity to define biologically and medicinally relevant chemical spaces. Profiling of chemical diversity, molecular complexity, and physicochemical properties of compound libraries using chemoinformatic approaches provide a solid basis to generate hypothesis of how to interrogate novel areas of chemical space for enhanced drug discovery. This commentary is focused on the application of chemoinformatic approaches to mine, and to navigate the chemical space of compound collections. The discussion is centered on the concept of chemical space, types of compound libraries used in drug discovery programs, applications of chemical space mining and visualization using chemoinformatic methods, and strategies to expand the pharmaceutical relevant chemical space with emphasis on the notion of molecular complexity.

关键词： chemoinformatics combinatorial libraries data mining molecular complexity natural products

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The impact of chemoinformatics on drug discovery in the pharmaceutical industry

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EXPERT OPINION ON DRUG DISCOVERY 2020年第3期15卷 293-306页

作者： Martinez-Mayorga, Karina Madariaga-Mazon, Abraham Medina-Franco, Jose L. Maggiora, Gerald Univ Nacl Autonoma Mexico Inst Quim Mexico City DF Mexico Univ Nacl Autonoma Mexico Fac Quim Mexico City DF Mexico Univ Arizona BIO5 Inst Tucson AZ USA

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field. Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field. Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

关键词： chemoinformatics polypharmacology polyspecificity molecular modeling artificial intelligence big data

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SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning

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BRIEFINGS IN BIOINFORMATICS 2017年第2期18卷 333-347页

作者： Wu, Zengrui Cheng, Feixiong Li, Jie Li, Weihua Liu, Guixia Tang, Yun Dana Farber Canc Inst Ctr Canc Syst Biol CCSB Harvard Med Sch 450 Brookline Ave Boston MA 02215 USA Northeastern Univ Ctr Complex Networks Res 110 Forsyth St Boston MA 02115 USA Sichuan Univ West China Hosp West China Med Sch State Key Lab Biotherapy Collaborat Innovat Ctr Chengdu 610041 Sichuan Peoples R China East China Univ Sci & Technol Shanghai Key Lab New Drug Design Sch Pharm 130 Meilong Rd Shanghai 200237 Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naive DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.

关键词： drug-target interaction drug repositioning network-based inference chemoinformatics failed drug chemical substructure

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Interactive Python Notebook Modules for chemoinformatics in Medicinal Chemistry

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JOURNAL OF CHEMICAL EDUCATION 2023年第12期100卷 4895-4902页

作者： Mahjour, Babak Mcgrath, Andrew Outlaw, Andrew Zhao, Ruheng Zhang, Charles Cernak, Tim Univ Michigan Coll Pharm Dept Med Chem Ann Arbor MI 48109 USA Univ Michigan Dept Chem Ann Arbor MI 48109 USA

Data science is becoming a mainstay in research. Despite this, very few STEM graduates matriculate with basic formal training in programming. The current lesson plan was developed to introduce undergraduates studying chemistry or biology to chemoinformatics and data science in medicinal chemistry. The objective of this lesson plan is to introduce students to common techniques used in analyzing medicinal chemistry data sets, such as visualizing chemical space, filtering to molecules that observe the Lipinski rules of drug-likeness, and principal component analysis. The content provided in this lesson plan is intended to serve as a tutorial-based reference for aspiring researchers. The lesson plan is split into two three-hour class sessions, each with an introductory slide deck, Python notebook consisting of several modules, and lab report template. During this activity, students learned to parse medicinal chemistry data sets with Python, perform simple machine learning analyses, and develop interactive graphs. During each session, students complete the Python notebook protocol and fill out a lab report template after a short lecture. By the end of the lesson plan, students were able to generate and manipulate various plots of chemical space and they reported having increased confidence in their understanding of chemistry, Python, and data science.

关键词： Upper-Division Undergraduate Python chemoinformatics Computer-Based Learning Medicinal Chemistry

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chemoinformatics And Computational Chemical Biology (Methods In Molecular Biology)

Chemoinformatics And Computational Chemical Biology (Methods...

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2010年

作者： Jürgen Bajorath

ISBN: (纸本)9781607618386

Over the past years, the chem(o)informatics field has further evolved and new application areas have opened up, for example, in the broadly defined area of chemical biology. In chemoinformatics and Computational Chemical Biology, leading investigators bring together a detailed series of reviews and methods including, among others, system-directed approaches using small molecules, the design of target-focused compound libraries, the study of molecular selectivity, and the systematic analysis of target-ligand interactions. Furthermore, the book delves into similarity methods, machine learning, probabilistic approaches, fragment-based methods, as well as topics that go beyond the current chemoinformatics spectrum, such as knowledge-based modeling of G protein-coupled receptor structures and computational design of siRNA libraries. As a volume in the highly successful Methods in Molecular Biology? series, this collection provides detailed descriptions and implementation advice that are exceedingly relevant for basic researchers and practitioners in this highly interdisciplinary research and development area. Cutting-edge and unambiguous, chemoinformatics and Computational Chemical Biology serves as an ideal guide for experts and newcomers alike to this vital and dynamic field of study

关键词： molecular biolog methods biology computational chemical chemoinformatics

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Bio/chemoinformatics in India: an outlook

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BRIEFINGS IN BIOINFORMATICS 2015年第4期16卷 710-731页

作者： Gupta, Shipra Chavan, Sonali Deobagkar, Dileep N. Deobagkar, Deepti D. Univ Pune Bioinformat Ctr Pune 411007 Maharashtra India Univ Pune Ctr Adv Studies Dept Zool Pune 411007 Maharashtra India

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.

关键词： bioinformatics chemoinformatics grid computing BTISnet education BINC

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Innovative genetic algorithms for chemoinformatics

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2002年第1-2期60卷 161-171页

作者： Lavine, BK Davidson, CE Moores, AJ Clarkson Univ Dept Chem Potsdam NY 13699 USA

In this paper, we report on the development of a genetic algorithm (GA) for pattern recognition analysis of multivariate chemical data. The GA identifies feature subsets that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is about differences between classes in the data set. The principal component (PC) plot function as embedded information filter. Sets of features are selected based on their principal component plots, with a good principal component plot generated by features whose variance or information is primarily about differences between classes in the data set. This limits the GA to search for these types of feature subsets, significantly reducing the size of the search space. In addition, the pattern recognition GA focuses on those classes and/or samples that are difficult to classify by boosting their weights over successive generation using a perceptron to team the class and sample weights. Samples that consistently classify correctly are not as heavily weighted in the analysis as samples that are difficult to classify. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection. The efficacy and efficiency of the pattern recognition GA is demonstrated via problems: from chemical communication and environmental analysis. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： genetic algorithms chemoinformatics perceptron

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A Combined Bioinformatics and chemoinformatics Approach for Developing Asymmetric Bivalent AMPA Receptor Positive Allosteric Modulators as Neuroprotective Agents

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CHEMMEDCHEM 2013年第2期8卷 226-230页

作者： Chen, Haijun Wang, Cheng Z. Ding, Chunyong Wild, Christopher Copits, Bryan Swanson, Geoffrey T. Johnson, Kenneth M. Zhou, Jia Univ Texas Med Branch Addict Res Ctr Dept Pharmacol & Toxicol Chem Biol Program Galveston TX 77555 USA Northwestern Univ Feinberg Sch Med Chicago IL 60611 USA

PAMs new in town! An effective, combined bioinformatics and chemoinformatics approach was applied to the design of novel asymmetric bivalent α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor positive allosteric modulators (PAMs) with marked potency in?vitro and efficacy in?vivo for preventing neuroapoptosis. The novel chemotype could provide pharmacological probes and potential therapeutic agents for glutamatergic hypofunction and its related neurological and psychiatric disorders.

关键词： bioinformatics biological space chemical space chemoinformatics drug design neuroprotective agents

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Milestones in chemoinformatics: global view of the field

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JOURNAL OF CHEMINFORMATICS 2024年第1期16卷 124页

作者： Bajorath, Jurgen Univ Bonn Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Over the past similar to 25 years, chemoinformatics has evolved as a scientific discipline, with a strong foundation in pharmaceutical research and scientific roots that can be traced back to the late 1950s. It covers a wide methodological spectrum and is perhaps best positioned in the greater context of chemical information science. Herein, the chemoinformatics discipline is delineated, characteristic (and partly problematic) features are discussed, and a global view of the field is provided, emphasizing key developments.

关键词： chemoinformatics Drug discovery Information science Chemical data Molecular design

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Artificial intelligence-open science symbiosis in chemoinformatics

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ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2024年 5卷

作者： Miljkovic, Filip Medina-Franco, Jose L. AstraZeneca Med Chem Res & Early Dev Cardiovasc Renal & Metab CVRM BioPharmaceut R&D Pepparedsleden 1 Gothenburg SE-43183 Sweden Univ Nacl Autonoma Mexico Sch Chem DIFACQUIM Res Grp Dept Pharm Ave Univ 3000 Mexico City 04510 Mexico

In chemoinformatics, artificial intelligence (AI) continues to grow a symbiosis with open science (OS). Such a close AI-OS interaction brings substantial practical benefits in research, scientific dissemination, and education, to name a few areas. The AI-OS symbiosis can be further enhanced by combining sufficient substantive expertise, mathematical and statistical knowledge, and coding skills. This Viewpoint discusses the benefits of the smooth and productive interaction between AI, OS, and open data. We also present a short list of misconceptions and pitfalls surrounding AI-OS and propose correct responses and behaviors agreed upon by field experts. In addition, we provide suggestions to continue enhancing the positive contributions of the AI-OS symbiosis towards chemoinformatics.

关键词： Artificial intelligence Open source Open data chemoinformatics Education

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