Lead Finder™ is equipped with a dedicated algorithm and scoring function for virtual screening. The algorithm is designed to rapidly dock and score ligands using a method that has been optimized to separate actives from inactives.
A study on the DUD dataset showed an impressive overall ROC AUC of 0.74 and a median ROC AUC of 0.76.
Flexible licensing terms enable you to use your entire cluster, maximizing your return on investment.
- Process thousands of molecules per hour
- Find novel leads through virtual screening
- Design focused libraries with a high enrichment of active compounds through screening of larger sets of virtual compounds
- Predict the 3D structure of active molecules
- Dock covalent and non-covalent ligands to your protein
- Rapidly assess new molecule designs for their fit to the protein active site
Lead Finder is a BioMolTech product distributed by Cresset.
References
- Stroganov O.V. et al., Lead Finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening, J Chem Inf Model. 2008 Dec;48(12):2371-85. PubMed ID: 19007114
- Novikov F. et al., CSAR scoring challenge reveals the need for new concepts in estimating protein-ligand binding affinity, J Chem Inf Model. 2011 Sep 26;51(9):2090-2096. PubMed ID: 21612285
- Novikov F.N. et al., Improving performance of docking-based virtual screening by structural filtration, J Mol Model. 2010 Jul;16(7):1223-30. Epub 2009 Dec 30
- Novikov F.N. et al., Lead Finder docking and virtual screening evaluation with Astex and DUD test sets, J Comput Aided Mol Des. 2012 Jun;26(6):725-35
- Zeifman A.A. et al., Hit clustering can improve virtual fragment screening: CDK2 and PARP1 case studies, J Mol Model. 2012 Jun;18(6):2553-66. Epub 2011 Nov 9
- Stroganov O.V. et al., TSAR, a new graph-theoretical approach to computational modeling of protein side-chain flexibility: Modeling of ionization properties of proteins. Proteins, 2011 Sep; 79(9):2693-2710. PubMed ID: 21769942
