Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences. Noteworthy, the structure and function of such systems can be studied and represented through the above-mentioned approaches, which range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to neuronal synapsis in the brain’s cortex, ecosystems, the internet, markets, social networks, program’s development in education, social learning, etc. On the other hand, ML algorithms are useful to study large datasets with characteristic features of complex systems. In this context, we decided to launch one special issue focused on the benefits of using ML and complex network analysis (in combination or separately) to study complex systems in applied sciences. The topic of the issue is: Complex Networks and Machine Learning in Applied Sciences. Contributions to this special issue are highlighted below. The present issue is also linked to conference series, MOL2NET International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI AG, SciForum, Basel, Switzerland. At the same time, the special issue and the conference are hosts for the works published by students/tutors of the USEDAT: USA–Europe Data Analysis Training Worldwide Program.

We are publishing the series of special issues “New experimental and computational tools for drug discovery” focused on the use of enabling technologies like Computational Chemistry, Bioinformatics, OMICS, Combinatorial Chemistry, Data Analysis, etc. in Medicinal Chemistry and Drug Discovery. The series have published a total of five special issues until this moment (1-5). The present issue (Part – VI) includes a new collection of papers exploring the use of Experimental techniques (LC-MS/MS, FTIR, or NMR, etc.) combined with computational methods like Molecular Docking and Machine Learning (ML) for the discovery of anti-cancer, anti-diabetic, and anti-leishmanial compounds.