en News - www.cheminformatics-nutrition.recerca.urv.cat News Fri, 25 Sep 2020 11:36:19 +0000 Fri, 25 Sep 2020 11:36:19 +0000 Houdini 2 (https://houdini.antaviana.cat/) / Experimental studies confirm the inhibitory capacity of SARS-CoV-2 replication of an antibiotic for veterinary use

A study by the URV's Chemoinformatics and Nutrition Research Group now shows that a new drug, sarafloxacin, also has inhibitory capabilities of M-pro (a key enzyme in the replication and transcription of the virus responsible for COVID-19 ). The European experimental validation initiative, COVID Moonshot, has studied it to check its bioactivity and the results confirm that it is the third drug of the seven identified by this research group that inhibits M-pro. Experimental results with sarafloxacin have also shown that its ability to slow down virus replication is even higher than the two anti-inflammatory drugs that were initially validated.

Fri, 25 Sep 2020 11:35:46 +0000 http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/20/experimental-studies-confirm-the-inhibitory-capacity-of-sars-cov-2-replication-of-an-antibiotic-for-veterinary-use http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/20 0
Two anti-inflammatory drugs found that inhibit the replication of the COVID-19 virus

A study by the URV's Cheminformatics and Nutrition research group has shown that an anti-inflammatory drug for humans and another for animals inhibit a key enzyme in the replication and transcription of the COVID-19 virus. The results of the work have been validated by the initiative COVIDMoonshot which has carried out in vitro studies and assessed their bioactivity. The results have been published in the "International Journal of Molecular Sciences"

Fri, 25 Sep 2020 11:36:19 +0000 http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/19/two-anti-inflammatory-drugs-found-that-inhibit-the-replication-of-the-covid-19-virus http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/19 0
Understanding the variability of the S1' pocket to improve matrix metalloproteinase inhibitor selectivity profiles.

Matrix metalloproteinases (MMPs) are a family of proteins involved in a range of pathologies. Given that MMP broad-spectrum inhibition is associated with severe adverse effects, selectivity has become a priority in the design of MMP inhibitors, and is often achieved by targeting the variable S1' pocket. However, the specific characteristics of the S1' pocket that determine inhibitor selectivity are often not described and, in many cases, challenging to identify. In this review, we investigate the variability of the S1' pocket across the MMP family, and propose explanations for the selectivity of previously described inhibitors. These analyses provide valuable insights into how to design novel inhibitors selective for a given MMP.

Tue, 24 Sep 2019 15:28:50 +0000 http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/18/understanding-the-variability-of-the-s1-pocket-to-improve-matrix-metalloproteinase-inhibitor-selectivity-profiles http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/18 0
Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors.

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

Tue, 24 Sep 2019 15:22:58 +0000 http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/17/mining-large-databases-to-find-new-leads-with-low-similarity-to-known-actives-application-to-find-new-dpp-iv-inhibitors http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/17 0
A new review about virtual screening published by the Cheminformatics and Nutrition Research Group

The Light and Dark Sides of Virtual Screening: What Is There to Know?  Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M, Pujadas G, Garcia-Vallvé S.  Int J Mol Sci. 2019 Mar 19;20(6). pii: E1375. doi: 10.3390/ijms20061375


Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

Tue, 24 Sep 2019 15:23:51 +0000 http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/16/a-new-review-about-virtual-screening-published-by-the-cheminformatics-and-nutrition-research-group http://www.cheminformatics-nutrition.recerca.urv.cat/en/news/16 0