Computational approaches in target identification and drug discovery

By Theodora Katsila, Georgios Spyroulias, George P. Patrinos, Minos-Timotheos Matsoukas. Computational and Structural Biotechnology Journal, 2016

323 citations recorded by the source corpus.

In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f770ea91cdbf38525990dfb4b62c79f83030e481

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In Computational Theory and Mathematics

1. Chanjin Jung
2. Jingyu Lee
3. Bilal Shaker
4. Dokyun Na
5. Sajjad Ahmad

In Molecular Biology

1. Chanjin Jung
2. Jingyu Lee
3. Bilal Shaker
4. Dokyun Na
5. Sajjad Ahmad

Citations in the corpus

• In silico methods and tools for drug discovery (2021, background): “A drug target is defined as a biological entity, usually a protein, that can modulate disease phenotypes [29].”