Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• a soft scoring function that assesses the complementarity between the surfaces of the ligand and the target. Thus, FlexAID has demonstrated superior performance compared to well-established software like AutoDock Vina, particularly when target flexibility plays a pivotal role, as is often the case

Computational methods in drug discovery

• Sumudu P. Leelananda and
• Steffen Lindert

Beilstein J. Org. Chem. 2016, 12, 2694–2718, doi:10.3762/bjoc.12.267

• ; machine learning; pharmacophore; QSAR; SBDD; scoring; target flexibility; Introduction Bringing a pharmaceutical drug to the market is a long term process that costs billions of dollars. In 2014, the Tufts Center for the Study of Drug Development estimated that the cost associated with developing and

• , agonists, inhibitors, etc. of a target) design. Molecular dynamics (MD) simulations are frequently used in SBDD to give insights into not only how ligands bind with target proteins but also the pathways of interaction and to account for target flexibility. This is especially important when drug targets are

• challenging process. Even when target flexibility is ignored there are still a huge number of ways a molecule can be docked. The total number of possible modes increases exponentially as the size of the two docked molecules increases. Therefore efficient search methods that are fast and effective, and