Special Issue "Deep Learning for Molecular Structure Modelling"
Deadline for manuscript submissions: 30 September 2020.
Interests: Novel algorithms in artificial intelligence and machine learning to bridge between computer and information science, engineering, and the life sciences; problem solving, search, planning, optimization, and learning for the simulation, analysis, and characterization of complex dynamic systems operating in the presence of constraints
Interests: Data mining and machine learning, especially on spatial, temporal, and network modeling and knolwedge discovery; deep learning on graphs; interpretable machine learning; gradient-free optimization for deep neural network training
The molecular structure is critical in determining chemical, physical, and biological properties. Despite significant research in molecular structure modeling over the last few decades, many outstanding challenges remain. From a computational perspective, molecules pose high-dimensional structural data that involve spatial, network, geometric, geodesic, and temporal characteristics which jointly determine molecular functions and properties. Grand challenges include how to concisely represent these characteristics while preserving all the useful structural information and how to efficiently and effectively map these structural characteristics to unknown, underlying properties and function(s). In recent decades, research in Artificial Intelligence, especially deep learning, has yielded impressive advancements in representing and generating high-dimensional data, such as sequence, image, and graph data, and in recent years, by representing molecules (and structures) as images or graphs, deep learning techniques have started to be utilized for molecular modeling; these techniques are drawing attention fast. Although encouraging results have been obtained in some applications, however, this line of research is in its infancy. The main aim of the Special Issue on “Deep Learning for Molecular Structure Modeling” is to be an open forum for researchers to share their findings via new techniques and applications. Specifically, we invite contributions that introduce new deep learning techniques towards better molecular (structure) characterization that takes into account spatial, network, and temporal properties under physical, biological, and chemical constraints. We also look forward to cutting-edge research on novel applications of deep learning for molecular modeling. Contributions to this issue, both in the form of original research or review articles, are welcome.
Prof. Dr. Amarda Shehu
Dr. Liang Zhao
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Molecular structure
- Modeling and analysis
- Deep learning
- Representation learning
- Deep generative models
- Deep learning on graphs
- Geometric deep learning
- Artificial intelligence
- Structural biology
- Structure prediction and design