Frontiers of Computer Science (FCS) was launched in 2007. It is published bimonthly both online and in print by HEP and Springer. It is indexed by SCI(E), EI, DBLP, Scopus, etc. The latest IF is 4.2, ranking among the top 30% of all journals in the field of Computer Science in WOS.

Drugs play a critical role in preventing and treating diseases. However, the process of discovering new drug candidates is challenging, time-consuming, and expensive, with a high failure rate. With the availability of big data on drugs, improving drug development processes comprehensively using computational techniques has become a hot research topic in computational and systems biology. Nowadays, Computational techniques have been widely applied throughout nearly every stage of the drug discovery and development workflow. Mining and integrating relevant data through computational techniques can help discover complex patterns that are difficult for humans to identify, and improve the speed and success rate of drug development. Recently, large datasets containing information on drugs' properties have been constructed, and there is a significant need to extract knowledge from these datasets and employ them to improve drug development processes comprehensively. Therefore, there is a strong motivation to develop powerful computational methods capable of efficiently mining these datasets, providing new predictions for experimental scientists, and narrowing down the scope of candidates to accelerate drug discovery. To validate potential prediction results with higher scores, biological experiments could be implemented.

To better support the application of computing technology in drug development, we encourage high-quality research that focuses on advancing and exchanging experience in applying computational techniques to drug discovery. This research should encompass fundamental theories, basic models, algorithms, and various use cases and applications. By sharing insights and research endeavors from different perspectives, this special section aims to inspire researchers from both industry and academia to contribute to this research direction and collectively drive progress in the field.

The topics include, but are not limited to, the following:

· Drug poor absorption, distribution, metabolism, excretion properties, and toxicities (ADMET) prediction;

· Drug-target interaction prediction;

· Drug-drug interaction prediction;

· Drug-disease association prediction;

· Drug-non-coding RNA association prediction;

· Drug-microbe association prediction;

· Adverse drug reaction prediction;

· Synergistic drug combinations prediction;

· Peptide therapeutic property prediction;

Timelines:

Submissions Deadline               : 31.12.2023

First Reviews Due                    : 15.02.2024

Second Reviews Due                : 31.03.2024

Notification of Final Decision     : 15.04.2024

Publication Date                      : 15.06.2024

Guest editor:

Jianxin Wang

Central south university, China             jxwang@mail.csu.edu.cn

Online submission:

http://mc.manuscriptcentral.com/hepfcs

Select the manuscript type as " Application of computational techniques in drug discovery ".

The template will also be found at this site.