46 research outputs found
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Graph Neural Networks for Molecules
Graph neural networks (GNNs), which are capable of learning representations
from graphical data, are naturally suitable for modeling molecular systems.
This review introduces GNNs and their various applications for small organic
molecules. GNNs rely on message-passing operations, a generic yet powerful
framework, to update node features iteratively. Many researches design GNN
architectures to effectively learn topological information of 2D molecule
graphs as well as geometric information of 3D molecular systems. GNNs have been
implemented in a wide variety of molecular applications, including molecular
property prediction, molecular scoring and docking, molecular optimization and
de novo generation, molecular dynamics simulation, etc. Besides, the review
also summarizes the recent development of self-supervised learning for
molecules with GNNs.Comment: A chapter for the book "Machine Learning in Molecular Sciences". 31
pages, 4 figure
Special Topics in Information Technology
This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis
Novel Antibacterial Agents
This book was devoted to the latest advances achieved in the antibacterial field, with a focus on the recent efforts made to develop new antimicrobial agents with novel modes of action, and a perspective on future directions of this line of research. Antimicrobial resistance has become a major threat to global health, and the twenty-two published articles here reported put in evidence that the discovery and development of new antibiotics are extremely challenging. The antimicrobial research covers a wide area, spanning from the design of new compounds, also supported by molecular modeling techniques, their synthesis and characterization, and biological tests.In this context, the current crisis caused by the COVID-19 pandemic, but also older threats, such as the human immunodeficiency virus or the hepatitis C virus, require greater attention than ever.The research works described in this book provide an extremely useful example of the results achieved in the field of antibacterial drug development. The search for new chemical entities was approached starting from both natural and synthetic compounds and addressing different targets. In addition, recent findings were presented and discussed highlighting the strategies to fight bacterial resistance. Detailed references to the state-of-the-art can be found in this book.We strongly encourage the wide group of readers to explore the book that we are presenting, to get inspired to develop new approaches for the diagnosis and treatment of antibacterial diseases, and to circumvent resistance issues