This book introduces “network pharmacology” as an emerging frontier subject of systematic drug research in the era of artificial intelligence and big data. Network Pharmacology is an original subject of fusion system biology, bioinformatics, network science and other related disciplines. It emphasizes on starting from the overall perspective of the system level and biological networks, the analysis of the laws of molecular association between drugs and their treatment objects, reveals the systematic pharmacological mechanisms of drugs, and guides the research and development of new drugs and clinical diagnosis and treatment. After it was proposed, network pharmacology has been paid attention by researchers, and it has been rapidly developed and widely used. In order to systematically reveal the biological basis of diagnosis and treatment in traditional Chinese medicine and modern medicine, we proposed a new concept of "network target" for the first time, which has become the core theory of "network pharmacology". The core principle of a network target is to construct a biological network that can be used to decipher complex diseases. The network is then used as the therapeutic target, to which multicomponent remedies are applied. This book mainly includes four parts: 1) The concept and theory of network pharmacology; 2) Common analysis methods, databases and software in network pharmacological research; 3) Typical cases of traditional Chinese medicine modernization and modern drug research based on network pharmacology; 4) Network pharmacology practice process based on drugs and diseases.

Phytochemicals from medicinal plants are receiving ever greater attention in the scientific literature, in medicine, and in the world economy in general. For example, the global value of plant-derived pharmaceuticals will reach $500 billion in the year 2000 in the OECD countries. In the developing countries, over-the-counter remedies and "ethical phytomedicines," which are standardized toxicologically and clinically defined crude drugs, are seen as a promising low cost alternatives in primary health care. The field also has benefited greatly in recent years from the interaction of the study of traditional ethnobotanical knowledge and the application of modem phytochemical analysis and biological activity studies to medicinal plants. The papers on this topic assembled in the present volume were presented at the annual meeting of the Phytochemical Society of North America, held in Mexico City, August 15-19, 1994. This meeting location was chosen at the time of entry of Mexico into the North American Free Trade Agreement as another way to celebrate the closer ties between Mexico, the United States, and Canada. The meeting site was the historic Calinda Geneve Hotel in Mexico City, a most appropriate site to host a group of phytochemists, since it was the address of Russel Marker. Marker lived at the hotel, and his famous papers on steroidal saponins from Dioscorea composita, which launched the birth control pill, bear the address of the hotel.

The global popularity of herbal supplements and the promise they hold in treating various disease states has caused an unprecedented interest in understanding the molecular basis of the biological activity of traditional remedies. Herbal Medicine: Biomolecular and Clinical Aspects focuses on presenting current scientific evidence of biomolecular ef

Despite considerable technological advances, the pharmaceutical industry is experiencing a severe innovation deficit, especially in the discovery of new drugs. Innovative Approaches in Drug Discovery: Ethnopharmacology, Systems Biology and Holistic Targeting provides a critical review and analysis of health, disease and medicine, and explores possible reasons behind the present crisis in drug discovery. The authors illustrate the benefits of systems biology and pharmacogenomics approaches, and advocate the expansion from disease-centric discovery to person-centric therapeutics involving holistic, multi-target, whole systems approaches. This book lays a path for reigniting pharmaceutical innovation through a disciplined reemergence of pharmacognosy, embracing open innovation models and collaborative, trusted public-private partnerships. With unprecedented advances made in the development of biomedically-relevant tools and technologies, the need is great and the time is now for a renewed commitment towards expanding the repertoire of medicines. By incorporating real-life examples and state-of-the-art reviews, this book provides valuable insights into the discovery and development strategies for professionals, academicians, and students in the pharmaceutical sciences. - Analyzes the reasons behind historical drug failures to provide valuable insights on lessons learned - Uses current scientific research to promote learning from traditional knowledge systems and through the integration of traditional and western medicines - Discusses advances in technologies and systems biology to support the transition from formulation discovery to therapeutic discovery

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.