Abstract
Virtual Ligand Screening (VLS) has become an integral part of the drug discovery process for many pharmaceutical companies. Ligand similarity searches provide a very powerful method of screening large databases of ligands to identify possible hits. If these hits belong to new chemotypes the method is deemed even more successful. eHiTS LASSO uses a new interacting surface point types (ISPT) molecular descriptor that is generated from the 3D structure of the ligand, but unlike most 3D descriptors it is conformation independent. Combined with a neural network machine learning technique, LASSO screens molecular databases at an ultra fast speed of 1 million structures in under 1 min on a standard PC. The results obtained from eHiTS LASSO trained on relatively small training sets of just 2, 4 or 8 actives are presented using the diverse directory of useful decoys (DUD) dataset. It is shown that over a wide range of receptor families, eHiTS LASSO is consistently able to enrich screened databases and provides scaffold hopping ability.
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Zsoldos Z, Reid D, Simon A, Sadjad SB, Johnson AP (2006) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 7:421–435
Moustakas DT, Lang PT, Pegg S, Pettersen E, Kuntz ID, Brooijmans N, Rizzo RC (2006) Development and validation of a modular, extensible docking program: DOCK 5. J Comput Aided Mol Des 20(10–11):601–619
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749
Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10–11):647–671
Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9(6):532–549
Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 47(10):2499–2510
Saeh JC, Lyne PD, Takasaki BK, Cosgrove DA (2005) Lead hopping using SVM and 3D pharmacophore fingerprints. J Chem Inf Model 45(4):1122–1133
Wagener M, van Geerestein VJ (2000) Potential drugs and nondrugs: prediction and identification of important structural features. J Chem Inf Comput Sci 40(2):280–292
Chen B, Harrison R, Papadatos G, Willett P, Wood D, Lewell X, Greenidge P, Stiefl N (2007) Evaluation of machine-learning methods for ligand-based virtual screening. J Comput Aided Mol Des 21(1):53–62
Harper G, Bradshaw J, Gittins JC, Green DV, Leach AR (2001) Prediction of biological activity for high-throughput screening using binary kernel discrimination. J Chem Inf Comput Sci 41(5):1295–1300
Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801
Clark T (2004) QSAR and QSPR based solely on surface properties? J Mol Graph Model 22(6):519–525
Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41(3):856–864
Brandstetter H, Kuhne A, Bode W, Huber R, von der Saal W, Wirthensohn K, Engh RA (1996) X-ray structure of active site-inhibited clotting factor Xa. Implications for drug design and substrate recognition. J Biol Chem 271(47):29988–29992
Zell A Stuttgart neural network simulator. University of Stuttgart. http://www-ra.informatik.unituebingen.de/SNNS/ (04/05/2005)
Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43(25):4759–4767
Pham TA, Jain AN (2006) Parameter estimation for scoring protein-ligand interactions using negative training data. J Med Chem 49(20):5856–5868
Zhang Q, Muegge I (2006) Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J Med Chem 49(5):1536–1548
McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas C, Lindsley S, Maiorov V, Truchon JF, Cornell WD (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47(4):1504–1519
Hawkins PC, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82
Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508
Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48(7):2534–2547
JChem v 3.2.4. http://www.chemaxon.com/jchem/ (06/20/2007)
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Reid, D., Sadjad, B.S., Zsoldos, Z. et al. LASSO—ligand activity by surface similarity order: a new tool for ligand based virtual screening. J Comput Aided Mol Des 22, 479–487 (2008). https://doi.org/10.1007/s10822-007-9164-5
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DOI: https://doi.org/10.1007/s10822-007-9164-5