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In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning.
Two main approaches involve the use of computational methodologies alongside suitable in vitro assays.
The use of computational methodologies can significantly lessen the time and expense required to bring the design of RNA-based nanoconstructs to experimental fruition.
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This review provides an overview of recent successful examples of computational methodologies used to predict druggable PPIs and small molecules designed to inhibit them.
However, the effective use of the massive amounts of cancer genome data remains a challenge due to the limitations of computational methodologies and insufficient collaboration and sharing.
New expectations on performance, coupled with the evolution of computational methodologies, are driving increasingly sophisticated solutions.
Finally, in Section 3.4 we discuss the development of computational methodologies that take advantage of the ReaxFF formalism to explore phenomena on time and length scales that are inaccessible to traditional MD simulation methodologies.
This special issue presents a broad spectrum of computational methodologies, biological investigation, and bioinformatics prediction.
The introduction of computational methodologies to vaccinology has enabled a significant step towards reverse vaccine design.
From the perspective of computational methodology, one prominent limitation of widely used methods is due to the fact that GWAS usually analyzes one single nucleotide polymorphism (SNP) at a time [ 3].
The present study represents the first use of this computational methodology for modeling nonlinear patterns in molecular genetics.
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