This book is for you! Why? Because this book is packed with best practices on many technical aspects of RESTful API Design, such as the correct use of resources, URIs, representations, content types, data formats, parameters, HTTP status codes and HTTP methods.
RESTful API Design: Best Practices In API Design With REST (API-University Series Book 3) Matthias B
1 RESTful API Design (API-University Series) (Volume 3) By Matthias Biehl RESTful API Design (API-University Series) (Volume 3) By Matthias Biehl Looking for best practices in RESTful APIs? This book is for you! Why? Because this book is packed with best practices on many technical aspects of RESTful API Design, such as the correct use of resources, URIs, representations, content types, data formats, parameters, HTTP status codes and HTTP methods. You want to design and develop APIs like a Pro? Use API description languages to both design APIs and develop APIs efficiently. The book introduces the two most common API description languages RAML and OpenAPI/Swagger. Your APIs connect to legacy systems? The book shows best practices for connecting APIs to existing backend systems. You expect lots of traffic on your API? The book shows you how to achieve high security, performance, availability and smooth evolution and versioning. Your company cares about its customers? Learn a customer-centric design and development approach for APIs, so you can design APIs as digital products. The API-University Series is a modular series of books on API-related topics. Each book focuses on a particular API topic, so you can select the topics within APIs, which are relevant for you. Keywords: RESTful, REST, API Design, API, API Description Languages, RAML, OpenAPI/Swagger Download RESTful API Design (API-University Series) (Volume...pdf Read Online RESTful API Design (API-University Series) (Volu...pdf
2 RESTful API Design (API-University Series) (Volume 3) By Matthias Biehl RESTful API Design (API-University Series) (Volume 3) By Matthias Biehl Looking for best practices in RESTful APIs? This book is for you! Why? Because this book is packed with best practices on many technical aspects of RESTful API Design, such as the correct use of resources, URIs, representations, content types, data formats, parameters, HTTP status codes and HTTP methods. You want to design and develop APIs like a Pro? Use API description languages to both design APIs and develop APIs efficiently. The book introduces the two most common API description languages RAML and OpenAPI/Swagger. Your APIs connect to legacy systems? The book shows best practices for connecting APIs to existing backend systems. You expect lots of traffic on your API? The book shows you how to achieve high security, performance, availability and smooth evolution and versioning. Your company cares about its customers? Learn a customer-centric design and development approach for APIs, so you can design APIs as digital products. The API-University Series is a modular series of books on API-related topics. Each book focuses on a particular API topic, so you can select the topics within APIs, which are relevant for you. Keywords: RESTful, REST, API Design, API, API Description Languages, RAML, OpenAPI/Swagger RESTful API Design (API-University Series) (Volume 3) By Matthias Biehl Bibliography Rank: # in Books Published on: Original language: English Dimensions: 9.00" h x.66" w x 6.00" l,.87 pounds Binding: Paperback 290 pages Download RESTful API Design (API-University Series) (Volume...pdf Read Online RESTful API Design (API-University Series) (Volu...pdf
Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.
A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates
Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis. 2ff7e9595c
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