The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

The widespread use of organoboron compounds justifies the efforts devoted to their synthesis, as well as toward developing an understanding of their reactivity. The nature of the mono- or diboron species is of paramount importance in determining the reversible covalent binding properties of the boron atom with both nucleophiles and electrophiles. By wedding the rich chemical potential of organoboron compounds to the ubiquity of organic scaffolds, advanced borylation reactions have the potential to open unprecedented synthetic alternatives, and new knowledge in the field should encourage chemists to use organoboron compounds. In this volume, the main objective is to provide a collection of the most useful, practical, and reliable methods, reported mainly within the last decade, for boron activation and boron reactivity. The volume covers the main concepts of organoboron compounds and includes experimental procedures, enabling newcomers to the field the instant and reliable application of the new tools in synthesis. Rather than aiming for a comprehensive coverage, the most advanced solutions for challenging transformations are introduced. To this end, a team of pioneers and leaders in the field have been assembled who discuss both the practical and conceptual aspects of this rapidly growing field.

The field of dual catalysis has developed rapidly over the last decade, and these volumes define its impact on organic synthesis. The most important, basic concepts of synergistic, dual catalytic cycles are introduced, providing newcomers to the field with reliable information on this new approach to facilitating the synthesis of organic molecules. Background information and reliable procedures for challenging transformations in synthesis are presented, applying the concept of cooperative dual catalysis as a means of increasing molecular complexity in the most efficient manner. The most useful, practical, and reliable methods for dual catalysis combining metal catalysts, organocatalysts, photocatalysts, and biocatalysts are presented.

The Science of Synthesis Editorial Board, together with the volume editors and authors, is constantly reviewing the whole field of synthetic organic chemistry as presented in Science of Synthesis and evaluating significant developments in synthetic methodology. Several annual volumes updating content across all categories ensure that you always have access to state-of-the-art synthetic methodology.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

The field of photocatalysis has developed rapidly over the last decade and it is time to clarify its impact on organic synthesis. This volume is an opportunity to provide the defining and current reference work for this field. A primary objective is to collect together the most useful, practical, and reliable methods for photocatalysis and to introduce them to a larger audience. The fundamental concepts of photophysics are introduduced and laboratory set-ups are described, enabling newcomers to the field to instantly apply these new tools in synthesis. Rather than aiming for comprehensive coverage, solutions for challenging transformations in synthesis applying visible light and suitable dyes are presented. A team of pioneers and leaders in the field has been assembled, who discuss both the practical and conceptual aspects of this rapidly growing field. Scope, limitations, and mechanism of the reactions are covered and key experimental procedures are included.