Automatic generation of 3D-atomic coordinates for organic molecules

Gasteiger, Johann; Rudolph, Christine; Sadowski, Jens

doi:10.1016/0898-5529(90)90156-3

Cited by 509 publications

(408 citation statements)

References 14 publications

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“…To satisfy the requirement of pharmacophore-based virtual screening, the two-dimensional (2D) structures (in SD form) of all the small molecules in the datasets were converted into three-dimensional (3D) structures with multi-conformers in Catalyst 3D format using the catDB utility program encoded in the Catalyst; the maximum number of conformers generated for each molecule was set to 250. For the sake of running docking-based virtual screening, the small molecules in the datasets were also transformed into three-dimensional MOL2 format using the Corina program [40] .…”

Section: Datasets Of Chemicalsmentioning

confidence: 99%

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Chen

Zhang

et al. 2009

Acta Pharmacol Sin

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Aim: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods. Methods: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors α (ERα), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS. Results: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS. Conclusion: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery. discovery, especially in the cases when no three-dimensional (3D) structural information on the protein target of interest is available. Even when the 3D structures of targets are known, PBVS is also used as a complementary approach to DBVS for pre-processing databases (libraries) of small molecules to remove compounds not possessing features known to be essential for binding or for post-filtering compounds selected by docking approaches [5] . A recent case study by Muthas et al indicated that post-filtering with pharmacophores was shown to increase enrichment rates in their investigated targets compared with docking alone [6] . Several studies have been performed to assess various DBVS methods and compare which docking programs are the most successful in identifying active hits [7][8][9][10] . The conclusion is that no docking program may outperform other docking programs for all the tested targets, and the performance of each tested docking program is highly dependent on the nature of the target binding site [5] .Nevertheless, few case studies for a direct comparison on the performances between PBVS and DBVS have been reported [11] . To gain a general view for the discrimination between these two types of approach in prioritizing actives from a database with decoys, we performed a benchmark comparison between the performances of PBVS and DBVS. Eight structurally diverse protein targets were selected in this study. The pharmacophore models were generated from the ligand co-crystallized complex structures of these targets using the LigandScout program [12] , and each PBVS was performed using the program Catalyst [13,14] . To avoid the target dependency of docking pro...

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Section: Datasets Of Chemicalsmentioning

confidence: 99%

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Chen

Zhang

et al. 2009

Acta Pharmacol Sin

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“…These were the semiempirical method AM1 18 as implemented in MOPAC93 (Software release 2002, Stewart, J. J. P.) and the ab initio method B3LYP/6-31G** as provided by the Gaussian 98 (Gaussian 98 revision A.7, Gaussian Inc., Pittsburgh, PA) package. All calculations included geometry optimization, employing CORINA 2001 22,23 to generate initial 3D structures from Simplified Molecular Input Line Entry Specification (SMILES) strings, followed by SYBYL force field geometry optimizations (SYBYL version 6.8, Tripos Associates, St. Louis, MO) before starting quantum chemical calculations. 16 Because no conformational search routines were employed it may well be that, in individual cases, the finally achieved geometry does not represent the energy minimum of the conformational space.…”

Section: Methodsmentioning

confidence: 99%

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

et al. 2005

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The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K ow ). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

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“…Before calculating MOE descriptors, single 3D conformers were generated by Corina. 16 CATS descriptors were calculated taking into consideration pairs of atom types separated by 0 up to 15 bonds. All descriptor columns were scaled to have zero mean and unit standard deviation.…”

Section: Methodsmentioning

confidence: 99%

SVM-Based Feature Selection for Characterization of Focused Compound Collections

Byvatov

Schneider

2004

J. Chem. Inf. Comput. Sci.

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Artificial neural networks, the support vector machine (SVM), and other machine learning methods for the classification of molecules are often considered as a "black box", since the molecular features that are most relevant for a given classifier are usually not presented in a human-interpretable form. We report on an SVM-based algorithm for the selection of relevant molecular features from a trained classifier that might be important for an understanding of ligand-receptor interactions. The original SVM approach was extended to allow for feature selection. The method was applied to characterize focused libraries of enzyme inhibitors. A comparison with classical Kolmogorov-Smirnov (KS)-based feature selection was performed. In most of the applications the SVM method showed sustained classification accuracy, thereby relying on a smaller number of molecular features than KS-based classifiers. In one case both methods produced comparable results. Limiting the calculation of descriptors to only the most relevant ones for a certain biological activity can also be used to speed up high-throughput virtual screening.

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Automatic generation of 3D-atomic coordinates for organic molecules

Cited by 509 publications

References 14 publications

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

SVM-Based Feature Selection for Characterization of Focused Compound Collections

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