Drug repurposing in silico screening platforms.

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.

Evolution of biophysical tools for quantitative protein interactions and drug discovery.

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.

Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

The role of chemical biology in the fight against SARS-CoV-2.

Since late 2019, biomedical labs all over the world have been struggling to cope with the 'new normal' and to find ways in which they can contribute to the fight against COVID-19. In this unique situation where a biomedical issue dominates people's lives and the news cycle, chemical biology has a great deal to contribute. This review will describe the importance of science at the chemistry/biology interface to both understand and combat the SARS-CoV-2 pandemic.