Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Abstract

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structureactivity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under:Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic drug delivery, drug discovery, target discoveryThe emergence of new computational technologies has had a significant impact on modern science, serving as a substantial contribution to scientific research. The primary goal of these new tools is to accelerate the process of research, using available computational resources, in different areas such as chemistry and biological biotechnology, among others, on such magnitude that they surpass human discernment. In this context, beginning in 1960, several scientists began to develop theories relating to molecular evolution, laying the basis for bioinformatics by publishing the first Atlas of Protein Sequences; this work is the

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