Cheminformatics Qsar And Machine Learning Applications For Novel Drug Development

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• Author : Kunal Roy
• Publisher : Elsevier
• Release Date : 2023-05-23
• Total pages : 768
• ISBN : 9780443186394
• File Size : 53,5 Mb
• Total Download : 729
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Download Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development in PDF, Epub, and Kindle

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book. The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases

• Author : Amalia Stefaniu,Azhar Rasul,Ghulam Hussain
• Publisher : BoD – Books on Demand
• Release Date : 2020-07-15
• Total pages : 192
• ISBN : 9781838800673
• File Size : 13,7 Mb
• Total Download : 478
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Download Cheminformatics and its Applications in PDF, Epub, and Kindle

Cheminformatics has emerged as an applied branch of Chemistry that involves multidisciplinary knowledge, connecting related fields such as chemistry, computer science, biology, pharmacology, physics, and mathematical statistics.The book is organized in two sections, including multiple aspects related to advances in the development of informatic tools and their specific use in compound structure databases with various applications in life sciences, mainly in medicinal chemistry, for identification and development of new therapeutically active molecules. The book covers aspects related to genomic analysis, semantic similarity, chemometrics, pattern recognition techniques, chemical reactivity prediction, drug-likeness assessment, bioavailability, biological target recognition, machine-based drug discovery and design. Results from various computational tools and methods are discussed in the context of new compound design and development, sharing promising opportunities, and perspectives.

• Author : Lodhi, Huma,Yamanishi, Yoshihiro
• Publisher : IGI Global
• Release Date : 2010-07-31
• Total pages : 418
• ISBN : 9781615209125
• File Size : 52,9 Mb
• Total Download : 931
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Download Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques in PDF, Epub, and Kindle

"This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.

• Author : Alexandre Varnek,Alex Tropsha
• Publisher : Royal Society of Chemistry
• Release Date : 2008
• Total pages : 356
• ISBN : 9780854041442
• File Size : 28,7 Mb
• Total Download : 275
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Download Chemoinformatics Approaches to Virtual Screening in PDF, Epub, and Kindle

Chemoinformatics is broadly a scientific discipline encompassing the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. It is distinct from other computational molecular modeling approaches in that it uses unique representations of chemical structures in the form of multiple chemical descriptors; has its own metrics for defining similarity and diversity of chemical compound libraries; and applies a wide array of statistical, data mining and machine learning techniques to very large collections of chemical compounds in order to establish robust relationships between chemical structure and its physical or biological properties. Chemoinformatics addresses a broad range of problems in chemistry and biology; however, the most commonly known applications of chemoinformatics approaches have been arguably in the area of drug discovery where chemoinformatics tools have played a central role in the analysis and interpretation of structure-property data collected by the means of modern high throughput screening. Early stages in modern drug discovery often involved screening small molecules for their effects on a selected protein target or a model of a biological pathway. In the past fifteen years, innovative technologies that enable rapid synthesis and high throughput screening of large libraries of compounds have been adopted in almost all major pharmaceutical and biotech companies. As a result, there has been a huge increase in the number of compounds available on a routine basis to quickly screen for novel drug candidates against new targets/pathways. In contrast, such technologies have rarely become available to the academic research community, thus limiting its ability to conduct large scale chemical genetics or chemical genomics research. However, the landscape of publicly available experimental data collection methods for chemoinformatics has changed dramatically in very recent years. The term "virtual screening" is commonly associated with methodologies that rely on the explicit knowledge of three-dimensional structure of the target protein to identify potential bioactive compounds. Traditional docking protocols and scoring functions rely on explicitly defined three dimensional coordinates and standard definitions of atom types of both receptors and ligands. Albeit reasonably accurate in many cases, conventional structure based virtual screening approaches are relatively computationally inefficient, which has precluded them from screening really large compound collections. Significant progress has been achieved over many years of research in developing many structure based virtual screening approaches. This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area of research towards its recognition as a predictive and decision support scientific discipline. The approaches discussed by the contributors to the monograph rely on chemoinformatics concepts such as: -representation of molecules using multiple descriptors of chemical structures -advanced chemical similarity calculations in multidimensional descriptor spaces -the use of advanced machine learning and data mining approaches for building quantitative and predictive structure activity models -the use of chemoinformatics methodologies for the analysis of drug-likeness and property prediction -the emerging trend on combining chemoinformatics and bioinformatics concepts in structure based drug discovery The chapters of the book are organized in a logical flow that a typical chemoinformatics project would follow - from structure representation and comparison to data analysis and model building to applications of structure-property relationship models for hit identification and chemical library design. It opens with the overview of modern methods of compounds library design, followed by a chapter devoted to molecular similarity analysis. Four sections describe virtual screening based on the using of molecular fragments, 2D pharmacophores and 3D pharmacophores. Application of fuzzy pharmacophores for libraries design is the subject of the next chapter followed by a chapter dealing with QSAR studies based on local molecular parameters. Probabilistic approaches based on 2D descriptors in assessment of biological activities are also described with an overview of the modern methods and software for ADME prediction. The book ends with a chapter describing the new approach of coding the receptor binding sites and their respective ligands in multidimensional chemical descriptor space that affords an interesting and efficient alternative to traditional docking and screening techniques. Ligand-based approaches, which are in the focus of this work, are more computationally efficient compared to structure-based virtual screening and there are very few books related to modern developments in this field. The focus on extending the experiences accumulated in traditional areas of chemoinformatics research such as Quantitative Structure Activity Relationships (QSAR) or chemical similarity searching towards virtual screening make the theme of this monograph essential reading for researchers in the area of computer-aided drug discovery. However, due to its generic data-analytical focus there will be a growing application of chemoinformatics approaches in multiple areas of chemical and biological research such as synthesis planning, nanotechnology, proteomics, physical and analytical chemistry and chemical genomics.

• Author : Satyabrata Jena,Dr. Narottam Pal, Dr. V Mohan Goud,Prof. (Dr.) KNV Rao
• Publisher : AG PUBLISHING HOUSE (AGPH Books)
• Release Date : 2022-11-16
• Total pages : 224
• ISBN : 9789395936750
• File Size : 43,6 Mb
• Total Download : 344
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Download AI to machine learning in Pharmaceuticals in PDF, Epub, and Kindle

The convergence of big data, artificial intelligence (AI), and machine learning (ML) has resulted in a paradigm change in the manner in which novel medications are generated and healthcare is given. It is vital to systematically harness data from varied sources and utilize digital technologies and sophisticated analytics in order to allow data-driven decision making in order to fully capitalize on the breakthroughs in technology that have been made in recent years. The field of data science is now in a position where it has an unparalleled chance to steer such a paradigm shift. This book provides a high-level overview of fundamental concepts in algorithmic theory, data representation techniques, and generative modelling. Use the discovery of antibiotics as a case study in machine learning applied to the production of drugs, and then examine several applications in drug-likeness prediction, antimicrobial resistance, & avenues for further investigation. In the most recent years, there has been a marked increase in the application of machine learning algorithms to the process of drug discovery, and this book offers a comprehensive overview of the rapidly developing field. An introduction to the ways in which machine learning iv and artificial intelligence are being used in the pharmaceutical industry. The introductory discussion focuses on the use of machine learning to better understand medication-target interactions as a means of enhancing drug delivery as well as healthcare and medical systems. In addition to this, give subjects on medication repurposing using machine learning, drug designing, and finally, address drug combinations that are recommended to patients who have several or complicated diseases.

• Author : Anil Kumar Saxena
• Publisher : Springer Nature
• Release Date : 2021-10-18
• Total pages : 402
• ISBN : 9783030852818
• File Size : 26,6 Mb
• Total Download : 844
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Download Biophysical and Computational Tools in Drug Discovery in PDF, Epub, and Kindle

This book reviews recent physicochemical and biophysical techniques applied in drug discovery research, and it outlines the latest advances in computational drug design. Divided into 10 chapters, the book discusses about the role of structural biology in drug discovery, and offers useful application cases of several biophysical and computational methods, including time-resolved fluorometry (TRF) with Förster resonance energy transfer (FRET), X-Ray crystallography, nuclear magnetic resonance spectroscopy, mass spectroscopy, generative machine learning for inverse molecular design, quantum mechanics/molecular mechanics (QM/MM,ONIOM) and quantum molecular dynamics (QMT) methods. Particular attention is given to computational search techniques applied to peptide vaccines using novel mathematical descriptors and structure and ligand-based virtual screening techniques in drug discovery research. Given its scope, the book is a valuable resource for students, researchers and professionals from pharmaceutical industry interested in drug design and discovery.

• Author : Muthukumarasamy Karthikeyan,Renu Vyas
• Publisher : Springer
• Release Date : 2014-05-06
• Total pages : 533
• ISBN : 9788132217800
• File Size : 15,8 Mb
• Total Download : 768
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Download Practical Chemoinformatics in PDF, Epub, and Kindle

Chemoinformatics is equipped to impact our life in a big way mainly in the fields of chemical, medical and material sciences. This book is a product of several years of experience and passion for the subject written in a simple lucid style to attract the interest of the student community who wish to master chemoinformatics as a career. The topics chosen cover the entire spectrum of chemoinformatics activities (methods, data and tools). The algorithms, open source databases, tutorials supporting theory using standard datasets, guidelines, questions and do it yourself exercises will make it valuable to the academic research community. At the same time every chapter devotes a section on development of new software tools relevant for the growing pharmaceutical, fine chemicals and life sciences industry. The book is intended to assist beginners to hone their skills and also constitute an interesting reading for the experts.

• Author : Navneet Sharma,Himanshu Ojha,Pawan Raghav,Ramesh K. Goyal
• Publisher : Academic Press
• Release Date : 2021-05-21
• Total pages : 510
• ISBN : 9780128217474
• File Size : 29,7 Mb
• Total Download : 505
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Download Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences in PDF, Epub, and Kindle

Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. Provides practical information on how to choose and use appropriate computational tools Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics

• Author : Kunal Roy,Supratik Kar,Rudra Narayan Das
• Publisher : Academic Press
• Release Date : 2015-03-03
• Total pages : 484
• ISBN : 9780128016336
• File Size : 24,7 Mb
• Total Download : 459
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Download Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment in PDF, Epub, and Kindle

Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. Includes numerous practical examples related to QSAR methods and applications Follows the Organization for Economic Co-operation and Development principles for QSAR model development Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools

• Author : Alexander Heifetz
• Publisher : Humana
• Release Date : 2022-11-05
• Total pages : 0
• ISBN : 1071617893
• File Size : 11,9 Mb
• Total Download : 120
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Download Artificial Intelligence in Drug Design in PDF, Epub, and Kindle

This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.

• Author : Shyamasree Ghosh,Rathi Dasgupta
• Publisher : Springer Nature
• Release Date : 2022-05-04
• Total pages : 337
• ISBN : 9789811688812
• File Size : 34,6 Mb
• Total Download : 623
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Download Machine Learning in Biological Sciences in PDF, Epub, and Kindle

This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.

• Author : Subhash C. Basak,Marjan Vračko
• Publisher : Elsevier
• Release Date : 2022-12-09
• Total pages : 503
• ISBN : 9780323857147
• File Size : 17,7 Mb
• Total Download : 497
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Download Big Data Analytics in Chemoinformatics and Bioinformatics in PDF, Epub, and Kindle

Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications. Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information. Brings together the current knowledge on the most important aspects of big data, including analysis using deep learning and fuzzy logic, transparency and data protection, disparate data analytics, and scalability of the big data domain Covers many applications of big data analysis in diverse fields such as chemistry, chemoinformatics, bioinformatics, computer-assisted drug/vaccine design, characterization of emerging pathogens, and environmental protection Highlights the considerable benefits offered by big data analytics to science, in biomedical fields and in industry

• Author : Alexandre Varnek
• Publisher : John Wiley & Sons
• Release Date : 2017-06-22
• Total pages : 488
• ISBN : 9781119137986
• File Size : 38,7 Mb
• Total Download : 975
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Download Tutorials in Chemoinformatics in PDF, Epub, and Kindle

30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods. Tutorials in Chemoinformatics contains more than 100 exercises in 30 tutorials exploring key topics and methods in the field. It takes an applied approach to the subject with a strong emphasis on problem-solving and computational methodologies. Each tutorial is self-contained and contains exercises for students to work through using a variety of software packages. The majority of the tutorials are divided into three sections devoted to theoretical background, algorithm description and software applications, respectively, with the latter section providing step-by-step software instructions. Throughout, three types of software tools are used: in-house programs developed by the authors, open-source programs and commercial programs which are available for free or at a modest cost to academics. The in-house software and data sets are available on a dedicated companion website. Key topics and methods covered in Tutorials in Chemoinformatics include: Data curation and standardization Development and use of chemical databases Structure encoding by molecular descriptors, text strings and binary fingerprints The design of diverse and focused libraries Chemical data analysis and visualization Structure-property/activity modeling (QSAR/QSPR) Ensemble modeling approaches, including bagging, boosting, stacking and random subspaces 3D pharmacophores modeling and pharmacological profiling using shape analysis Protein-ligand docking Implementation of algorithms in a high-level programming language Tutorials in Chemoinformatics is an ideal supplementary text for advanced undergraduate and graduate courses in chemoinformatics, bioinformatics, computational chemistry, computational biology, medicinal chemistry and biochemistry. It is also a valuable working resource for medicinal chemists, academic researchers and industrial chemists looking to enhance their chemoinformatics skills.

• Author : Kunal Roy
• Publisher : Springer
• Release Date : 2017-05-22
• Total pages : 555
• ISBN : 9783319568508
• File Size : 19,9 Mb
• Total Download : 481
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Download Advances in QSAR Modeling in PDF, Epub, and Kindle

The book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool. This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.

• Author : Jürgen Bajorath
• Publisher : John Wiley & Sons
• Release Date : 2013-09-25
• Total pages : 432
• ISBN : 9781118743096
• File Size : 34,8 Mb
• Total Download : 511
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Download Chemoinformatics for Drug Discovery in PDF, Epub, and Kindle

Chemoinformatics strategies to improve drug discoveryresults With contributions from leading researchers in academia and thepharmaceutical industry as well as experts from the softwareindustry, this book explains how chemoinformatics enhances drugdiscovery and pharmaceutical research efforts, describing whatworks and what doesn't. Strong emphasis is put on tested and provenpractical applications, with plenty of case studies detailing thedevelopment and implementation of chemoinformatics methods tosupport successful drug discovery efforts. Many of these casestudies depict groundbreaking collaborations between academia andthe pharmaceutical industry. Chemoinformatics for Drug Discovery is logicallyorganized, offering readers a solid base in methods and models andadvancing to drug discovery applications and the design ofchemoinformatics infrastructures. The book features 15 chapters,including: What are our models really telling us? A practical tutorial onavoiding common mistakes when building predictive models Exploration of structure-activity relationships and transfer ofkey elements in lead optimization Collaborations between academia and pharma Applications of chemoinformatics in pharmaceuticalresearch—experiences at large international pharmaceuticalcompanies Lessons learned from 30 years of developing successfulintegrated chemoinformatic systems Throughout the book, the authors present chemoinformaticsstrategies and methods that have been proven to work inpharmaceutical research, offering insights culled from their owninvestigations. Each chapter is extensively referenced withcitations to original research reports and reviews. Integrating chemistry, computer science, and drug discovery,Chemoinformatics for Drug Discovery encapsulates the fieldas it stands today and opens the door to further advances.

• Author : Adriano D. Andricopulo,Leonardo L. G. Ferreira
• Publisher : Frontiers Media SA
• Release Date : 2019-02-05
• Total pages : 415
• ISBN : 9782889457441
• File Size : 10,9 Mb
• Total Download : 236
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Download Chemoinformatics Approaches to Structure- and Ligand-Based Drug Design in PDF, Epub, and Kindle

Chemoinformatics is paramount to current drug discovery. Structure- and ligand-based drug design strategies have been used to uncover hidden patterns in large amounts of data, and to disclose the molecular aspects underlying ligand-receptor interactions. This Research Topic aims to share with a broad audience the most recent trends in the use of chemoinformatics in drug design. To that end, experts in all areas of drug discovery have made their knowledge available through a series of articles that report state-of-the-art approaches. Readers are provided with outstanding contributions focusing on a wide variety of topics which will be of great value to those interested in the many different and exciting facets of drug design.

• Author : Ke Wu,Bharath Natarajan,Lisa Morkowchuk,Mike Krein,Curt M. Breneman
• Publisher : Elsevier Inc. Chapters
• Release Date : 2013-07-10
• Total pages : 542
• ISBN : 9780128059463
• File Size : 10,7 Mb
• Total Download : 289
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Download Materials Science and Engineering in PDF, Epub, and Kindle

The Materials Genome Initiative (MGI) was conceived as a unified effort to capture, curate, and exploit materials structure/property information on a grand scale to enable rapid, cost-effective development of novel materials with predictable properties. While the use of “genomic” methods to facilitate property prediction, virtual design, and discovery of materials is relatively new, the concepts driving the development of materials informatics are based, solidly, on the lessons learned during the development history of cheminformatics and bioinformatics. This chapter describes some of the ways in which cheminformatics and machine learning methods have been adapted for, and utilized in, materials science and engineering applications. Examples of how materials quantitative structure–property relationship (MQSPR) models are created, validated, and utilized are presented.

• Author : Vivek Chavda,K. Anand,Vasso Apostolopoulos
• Publisher : John Wiley & Sons
• Release Date : 2023-03-14
• Total pages : 452
• ISBN : 9781119865117
• File Size : 12,5 Mb
• Total Download : 673
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Download Bioinformatics Tools for Pharmaceutical Drug Product Development in PDF, Epub, and Kindle

BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries. The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall. Readers will find in this book: Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.

• Author : Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
• Publisher : Springer
• Release Date : 2019-02-25
• Total pages : 383
• ISBN : 9783030114794
• File Size : 28,6 Mb
• Total Download : 678
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Download Handbook of Deep Learning Applications in PDF, Epub, and Kindle

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

• Author : Huixiao Hong
• Publisher : Springer Nature
• Release Date : 2023-03-11
• Total pages : 654
• ISBN : 9783031207303
• File Size : 28,7 Mb
• Total Download : 487
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Download Machine Learning and Deep Learning in Computational Toxicology in PDF, Epub, and Kindle

This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.

• Author : Nathan Brown
• Publisher : Royal Society of Chemistry
• Release Date : 2020-11-11
• Total pages : 406
• ISBN : 9781839160547
• File Size : 22,9 Mb
• Total Download : 577
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Download Artificial Intelligence in Drug Discovery in PDF, Epub, and Kindle

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.