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Journal of Computer-Aided Molecular Design, Volume 30
Volume 30, Number 1, January 2016
- Mengjun Wu, Martin Vogt, Gerald M. Maggiora
, Jürgen Bajorath:
Design of chemical space networks on the basis of Tversky similarity. 1-12 - Justyna Bednarko, Justyna Wielinska, Karol Sikora
Theoretical studies on the reaction of mono- and ditriflate derivatives of 1, 4: 3, 6-dianhydro-d-mannitol with trimethylamine - Can a quaternary ammonium salt be a source of the methyl group? 13-26 - Hua-Jun Luo, Jun-Zhi Wang, Nian-Yu Huang
Computational insights into the interaction mechanism of triazolyl substituted tetrahydrobenzofuran derivatives with H+, K+-ATPase at different pH. 27-37 - Rand Shahin
Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets. 39-68 - Raoni Almeida de Souza, Natalia Díaz
Unraveling the distinctive features of hemorrhagic and non-hemorrhagic snake venom metalloproteinases using molecular simulations. 69-83 - Cecylia S. Lupala
New insights into the stereochemical requirements of the bradykinin B2 receptor antagonists binding. 85-101
Volume 30, Number 2, February 2016
- Anthony Nicholls
Confidence limits, error bars and method comparison in molecular modeling. Part 2: comparing methods. 103-126 - Ann E. Cleves
Extrapolative prediction using physically-based QSAR. 127-152 - Nikolaos N. Louros
A β-solenoid model of the Pmel17 repeat domain: insights to the formation of functional amyloid fibrils. 153-164 - Anna Rybinska
Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids. 165-176 - Jeffrey L. Mendenhall
Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout. 177-189
Volume 30, Number 3, March 2016
- Martin Vogt, Dagmar Stumpfe, Gerald M. Maggiora
Lessons learned from the design of chemical space networks and opportunities for new applications. 191-208 - Gregory Sliwoski, Jeffrey L. Mendenhall, Jens Meiler:
Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign. 209-217 - Zhiqiang Yan
Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks. 219-227 - Swapnil Chavan, Ahmed Abdelaziz, Jesper G. Wiklander
A k-nearest neighbor classification of hERG K+ channel blockers. 229-236 - Mohammad Mahdi Jaghoori, Boris Bleijlevens, Sílvia Delgado Olabarriaga:
1001 Ways to run AutoDock Vina for virtual screening. 237-249 - Erika Piccirillo
Conformational flexibility of DENV NS2B/NS3pro: from the inhibitor effect to the serotype influence. 251-270 - Sam Lear
Pep-Calc.com: a set of web utilities for the calculation of peptide and peptoid properties and automatic mass spectral peak assignment. 271-277
Volume 30, Number 4, April 2016
- Anthony Nicholls
Statistics in molecular modeling: a summary. 279-280 - Wendy A. Warr:
MOOCers and shakers and chemistry course takers. 281-284 - Eleonora Gianti, Troy E. Messick
Computational analysis of EBNA1 "druggability" suggests novel insights for Epstein-Barr virus inhibitor design. 285-303 - Lucas N. Alberca, María L. Sbaraglini
Discovery of novel polyamine analogs with anti-protozoal activity by computer guided drug repositioning. 305-321 - Takako Takeda, Yanli Wang, Stephen H. Bryant:
Structural insights of a PI3K/mTOR dual inhibitor with the morpholino-triazine scaffold. 323-330 - Jon G. Wilkes, Iva B. Stoyanova-Slavova, Dan A. Buzatu:
Alignment-independent technique for 3D QSAR analysis. 331-345 - Sehan Lee, Mace G. Barron:
A mechanism-based 3D-QSAR approach for classification and prediction of acetylcholinesterase inhibitory potency of organophosphate and carbamate analogs. 347-363
Volume 30, Number 5, May 2016
- María Garrido-Arandia
Computational study of pH-dependent oligomerization and ligand binding in Alt a 1, a highly allergenic protein with a unique fold. 365-379 - Jaroslaw Polanski
The analysis of the market success of FDA approvals by probing top 100 bestselling drugs. 381-389 - Johannes R. Loeffler, Emanuel S. R. Ehmki
Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design. 391-400 - Shweta Kulshreshtha
Computational approaches for predicting mutant protein stability. 401-412 - Zhi-Jiang Yao, Jie Dong
TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. 413-424 - Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu:
Ring system-based chemical graph generation for de novo molecular design. 425-446
Volume 30, Number 6, June 2016
- Andrew Anighoro, Jürgen Bajorath:
Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor. 447-456 - Ashutosh Kumar
A pose prediction approach based on ligand 3D shape similarity. 457-469 - Patcharapong Thangsunan
Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches. 471-488 - Fotis A. Baltoumas
Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions. 489-512 - Antonio Cardone, Mary Brady, Ram D. Sriram, Harish C. Pant, Sergio A. Hassan:
Computational study of the inhibitory mechanism of the kinase CDK5 hyperactivity by peptide p5 and derivation of a pharmacophore. 513-521
Volume 30, Number 7, July 2016
- Ryo Kunimoto
Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. 523-531 - Yuan Hu
The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP. 533-539 - Rodrigo Ochoa
Drug search for leishmaniasis: a virtual screening approach by grid computing. 541-552 - Juan Li, Huisheng Fang:
A comparison of different functions for predicted protein model quality assessment. 553-558 - Alexey V. Rossokhin
Side chain flexibility and the pore dimensions in the GABAA receptor. 559-567 - Onur Serçinoglu
A computational docking study on the pH dependence of peptide binding to HLA-B27 sub-types differentially associated with ankylosing spondylitis. 569-581
Volume 30, Number 8, August 2016
- Mathias M. von Behren, Stefan Bietz, Eva Nittinger
mRAISE: an alternative algorithmic approach to ligand-based virtual screening. 583-594 - Steven Kearnes
Molecular graph convolutions: moving beyond fingerprints. 595-608 - Steven Kearnes
ROCS-derived features for virtual screening. 609-617 - Agustina Arroyuelo
Azahar: a PyMOL plugin for construction, visualization and analysis of glycan molecules. 619-624 - Nam-Chul Cho, Seoung-Hwan Seo, Dohee Kim, Ji-Sun Shin, Jeongmin Ju, Jihye Seong
Pharmacophore-based virtual screening, biological evaluation and binding mode analysis of a novel protease-activated receptor 2 antagonist. 625-637 - Nguyen Quoc Thai
Discovery of DNA dyes Hoechst 34580 and 33342 as good candidates for inhibiting amyloid beta formation: in silico and in vitro study. 639-650
Volume 30, Number 9, September 2016
- Symon Gathiaka, Shuai Liu, Michael Chiu
D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions. 651-668 - Inna Slynko, Franck Da Silva, Guillaume Bret, Didier Rognan
Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015. 669-683 - Ashutosh Kumar
Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015. 685-693 - Zhaofeng Ye, Matthew P. Baumgartner
Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge. 695-706 - Majda Misini Ignjatovic, Octav Caldararu, Geng Dong
Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. 707-730 - Diogo Santos-Martins
Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015. 731-742 - Nan-Jie Deng
Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015. 743-751 - Stefano Piotto
Yada: a novel tool for molecular docking calculations. 753-759 - Jocelyn Sunseri
A D3R prospective evaluation of machine learning for protein-ligand scoring. 761-771 - Veronica Salmaso
DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015. 773-789 - Sergei Grudinin
Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. 791-804 - Sergio Ruiz-Carmona
Docking-undocking combination applied to the D3R Grand Challenge 2015. 805-815 - Philip Prathipati
Improved pose and affinity predictions using different protocols tailored on the basis of data availability. 817-828 - Edithe Selwa, Virginie Y. Martiny, Bogdan I. Iorga
Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets. 829-839
Volume 30, Number 10, October 2016
- Andrew Anighoro, Antonio de la Vega de León
Predicting bioactive conformations and binding modes of macrocycles. 841-849 - Jacopo Sgrignani, Filomena De Luca, Hayarpi Torosyan
Structure-based approach for identification of novel phenylboronic acids as serine-β-lactamase inhibitors. 851-861 - Eelke B. Lenselink, Thijs Beuming, Corine van Veen, Arnault Massink, Woody Sherman
In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor. 863-874 - Takeshi Ishikawa
Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation. 875-887 - Hui Zhang, Lan Ding, Yi Zou, Shui-Qing Hu, Hai-Guo Huang, Wei-Bao Kong, Ji Zhang:
Predicting drug-induced liver injury in human with Naïve Bayes classifier approach. 889-898 - Bipin Singh
Effects of point mutations on the thermostability of B. subtilis lipase: investigating nonadditivity. 899-916 - Jiraphorn Phanich, Thanyada Rungrotmongkol, Nawee Kungwan
Role of R292K mutation in influenza H7N9 neuraminidase toward oseltamivir susceptibility: MD and MM/PB(GB)SA study. 917-926
Volume 30, Number 11, November 2016
- Caitlin C. Bannan
Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge. 927-944 - Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianwen A. Feng, Daniel F. Ortwine, David L. Mobley, John D. Chodera
Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge. 945-958 - Andreas Klamt, Frank Eckert, Jens Reinisch, Karin Wichmann:
Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set. 959-967 - Samuel Genheden
All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5. 969-976 - Ganesh Kamath, Igor Kurnikov, Boris Fain, Igor V. Leontyev, Alexey A. Illarionov, Oleg Butin, Michael A. Olevanov, Leonid B. Pereyaslavets
Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and ARROW polarizable force fields. 977-988 - Gerhard König
Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge. 989-1006 - Sebastian Diaz-Rodriguez, Samantha M. Bozada, Jeremy R. Phifer, Andrew S. Paluch
Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model. 1007-1017 - Kee-Choo Chung, Hwangseo Park:
Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules. 1019-1033 - Nicolas Tielker, Daniel Tomazic, Jochen Heil, Thomas Kloss, Sebastian Ehrhart
The SAMPL5 challenge for embedded-cluster integral equation theory: solvation free energies, aqueous pK a, and cyclohexane-water log D. 1035-1044 - Ian M. Kenney, Oliver Beckstein
Prediction of cyclohexane-water distribution coefficients for the SAMPL5 data set using molecular dynamics simulations with the OPLS-AA force field. 1045-1058 - S. Shanaka Paranahewage, Cassidy S. Gierhart, Christopher J. Fennell
Predicting water-to-cyclohexane partitioning of the SAMPL5 molecules using dielectric balancing of force fields. 1059-1065 - Emiliano Brini
Adapting the semi-explicit assembly solvation model for estimating water-cyclohexane partitioning with the SAMPL5 molecules. 1067-1077 - Diogo Santos-Martins
Calculation of distribution coefficients in the SAMPL5 challenge from atomic solvation parameters and surface areas. 1079-1086 - Frank C. Pickard IV
Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. 1087-1100 - Stefano Bosisio, Antonia S. J. S. Mey, Julien Michel:
Blinded predictions of distribution coefficients in the SAMPL5 challenge. 1101-1114 - Tyler Luchko
SAMPL5: 3D-RISM partition coefficient calculations with partial molar volume corrections and solute conformational sampling. 1115-1127 - Michael R. Jones, Bernard R. Brooks, Angela K. Wilson
Partition coefficients for the SAMPL5 challenge using transfer free energies. 1129-1138
Volume 30, Number 12, December 2016
- Bradley Sherborne, Veerabahu Shanmugasundaram
Collaborating to improve the use of free-energy and other quantitative methods in drug discovery. 1139-1141 - Wendy A. Warr:
Blowing a breath of fresh share on data. 1143-1147 - Mámon M. Hatmal
Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: beta-secretase 1 and check point kinase 1 as case studies. 1149-1163 - Hugo de Almeida
Identification of novel Trypanosoma cruzi prolyl oligopeptidase inhibitors by structure-based virtual screening. 1165-1174 - Remigijus Didziapetris, Kiril Lanevskij
Compilation and physicochemical classification analysis of a diverse hERG inhibition database. 1175-1188 - Hongli Liu, Rui Han, Jiazhong Li, Huanxiang Liu
Molecular mechanism of R-bicalutamide switching from androgen receptor antagonist to agonist induced by amino acid mutations using molecular dynamics simulations and free energy calculation. 1189-1200
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