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Journal of Computer-Aided Molecular Design, Volume 34
Volume 34, Number 1, January 2020
- Filip Miljkovic
, Jürgen Bajorath
Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome. 1-10 - Xiaoqian Xu
Inhibitor discovery for the E. coli meningitis virulence factor IbeA from homology modeling and virtual screening. 11-25 - Chonnikan Hanpaibool, Matina Leelawiwat, Kaito Takahashi
Source of oseltamivir resistance due to single E119D and double E119D/H274Y mutations in pdm09H1N1 influenza neuraminidase. 27-37 - Radhika Vangala, Sree Kanth Sivan
Computational design, synthesis and evaluation of new sulphonamide derivatives targeting HIV-1 gp120. 39-54 - Maxim Gureev, Daria Novikova
Simulation of MDM2 N-terminal domain conformational lability in the presence of imidazoline based inhibitors of MDM2-p53 protein-protein interaction. 55-70 - Andreas Mecklenfeld
Applicability of a thermodynamic cycle approach for a force field parametrization targeting non-aqueous solvation free energies. 71-82 - Lakshmi Maganti, Dhananjay Bhattacharyya
Sequence specificity in DNA-drug intercalation: MD simulation and density functional theory approaches. 83-95
Volume 34, Number 2, February 2020
- Michael K. Gilson, Rommie E. Amaro
Drug Design Data Resource, Grand Challenge 4, second of two issues. 97 - Conor D. Parks, Zied Gaieb, Michael Chiu, Huanwang Yang, Chenghua Shao
D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. 99-119 - Oleg V. Stroganov
The role of human in the loop: lessons from D3R challenge 4. 121-130 - Duc Duy Nguyen
MathDL: mathematical deep learning for D3R Grand Challenge 4. 131-147 - Andrea Basciu
Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4. 149-162 - Sukanya Sasmal
D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors. 163-177 - Sergei Kotelnikov, Andrey Alekseenko
Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. 179-189 - Maria Kadukova
Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. 191-200 - Bo Wang, Ho-Leung Ng
Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4. 201-217
Volume 34, Number 3, March 2020
- George Nicola
Druggable exosites of the human kino-pocketome. 219-230 - Gustav Olanders
Conformational analysis of macrocycles: comparing general and specialized methods. 231-252 - Alberto Mills
Atomistic insight into sequence-directed DNA bending and minicircle formation propensity in the absence and presence of phased A-tracts. 253-265 - Jesús Sánchez-Márquez
Software to obtain spatially localized functions from different radial functions. 267-280 - G. I. Makarov
A noncanonical binding site of linezolid revealed via molecular dynamics simulations. 281-291 - Martiniano Bello
Dissecting the molecular recognition of dual lapatinib derivatives for EGFR/HER2. 293-303 - Gonzalo Cerruela García
Influence of feature rankers in the construction of molecular activity prediction models. 305-325
Volume 34, Number 4, April 2020
- Maximiliano Riquelme, Esteban Vöhringer-Martinez
SAMPL6 Octanol-water partition coefficients from alchemical free energy calculations with MBIS atomic charges. 327-334 - Mehtap Isik
Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge. 335-370 - Piero Procacci
SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches. 371-384 - Christoph Loschen
COSMO-RS based predictions for the SAMPL6 logP challenge. 385-392 - Shuzhe Wang
Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge. 393-403 - Mehtap Isik
Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. 405-420 - Ye Ding
Predicting partition coefficients of drug-like molecules in the SAMPL6 challenge with Drude polarizable force fields. 421-435 - Alexei M. Nikitin
Non-zero Lennard-Jones parameters for the Toukan-Rahman water model: more accurate calculations of the solvation free energy of organic substances. 437-441 - William J. Zamora
Prediction of the n-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations. 443-451 - Nicolas Tielker, Daniel Tomazic, Lukas Eberlein, Stefan Güssregen, Stefan M. Kast
The SAMPL6 challenge on predicting octanol-water partition coefficients from EC-RISM theory. 453-461 - Evrim Arslan, Basak Koca Findik
A blind SAMPL6 challenge: insight into the octanol-water partition coefficients of drug-like molecules via a DFT approach. 463-470
Volume 34, Number 5, May 2020
- Andreas Krämer
Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water-octanol partition coefficients in the SAMPL6 challenge. 471-483 - Michael R. Jones
Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge. 485-493 - Prajay Patel
SAMPL6 logP challenge: machine learning and quantum mechanical approaches. 495-510 - Davy Guan
LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules. 511-522 - Raymond Lui
A comparison of molecular representations for lipophilicity quantitative structure-property relationships with results from the SAMPL6 logP Prediction Challenge. 523-534 - Samarjeet Prasad
A deep learning approach for the blind logP prediction in SAMPL6 challenge. 535-542 - Shujie Fan
Prediction of octanol-water partition coefficients for the SAMPL6-log P molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields. 543-560 - Marilyn R. Gunner
Standard state free energies, not pKas, are ideal for describing small molecule protonation and tautomeric states. 561-573 - Jonathan A. Ouimet, Andrew S. Paluch
Predicting octanol/water partition coefficients for the SAMPL6 challenge using the SM12, SM8, and SMD solvation models. 575-588 - Zhaoxi Sun
SAMPL6 host-guest binding affinities and binding poses from spherical-coordinates-biased simulations. 589-600 - Andrea Rizzi
The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. 601-633
Volume 34, Number 6, June 2020
- Piero Procacci:
A remark on the efficiency of the double-system/single-box nonequilibrium approach in the SAMPL6 SAMPLing challenge. 635-639 - Vikram Khanna, Jacob I. Monroe
Performing solvation free energy calculations in LAMMPS using the decoupling approach. 641-646 - Takeshi Ashida, Takeshi Kikuchi:
A new method for estimating the relative binding free energy, derived from a free energy variational principle for the Pim-1-kinase-ligand and FKBP-ligand systems. 647-658 - Edgar López-López
Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach. 659-669 - Ravi Kumar Rajan
Identification and neuroprotective evaluation of a potential c-Jun N-terminal kinase 3 inhibitor through structure-based virtual screening and in-vitro assay. 671-682 - Petr Kovár, Jakub Skoda, Miroslav Pospísil
How N-(pyridin-4-yl)pyridin-4-amine and its methyl and nitro derivatives are arranged in the interlayer space of zirconium sulfophenylphosphonate: a problem solved by experimental and calculation methods. 683-695 - Kamil J. Kuder
A Taxicab geometry quantification system to evaluate the performance of in silico methods: a case study on adenosine receptors ligands. 697-707
Volume 34, Number 7, July 2020
- Nathan Brown, Peter Ertl
Artificial intelligence in chemistry and drug design. 709-715 - Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction. 717-730 - Henry E. Webel
Revealing cytotoxic substructures in molecules using deep learning. 731-746 - Darren V. S. Green
BRADSHAW: a system for automated molecular design. 747-765 - Darren V. S. Green
Correction to: BRADSHAW: a system for automated molecular design. 767 - Zhonghua Xia
Focused Library Generator: case of Mdmx inhibitors. 769-782 - Gian Marco Ghiandoni
Enhancing reaction-based de novo design using a multi-label reaction class recommender. 783-803 - Arkadii I. Lin
Diversifying chemical libraries with generative topographic mapping. 805-815
Volume 34, Number 8, August 2020
- Anjali Soni
Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. 817-830 - Anita Rácz, György M. Keserü:
Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling. 831-839 - Muhammad Arif
TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. 841-856 - Yudibeth Sixto-López
Exploring the inhibitory activity of valproic acid against the HDAC family using an MMGBSA approach. 857-878 - Huiqun Wang, Bethany A. Reinecke, Yan Zhang
Computational insights into the molecular mechanisms of differentiated allosteric modulation at the mu opioid receptor by structurally similar bitopic modulators. 879-895 - Roberto Paciotti
Characterization of PD-L1 binding sites by a combined FMO/GRID-DRY approach. 897-914 - Bani Kumar Pathak, Debajyoti Das, Sayan Bhakta, Partha Chakrabarti, Jayati Sengupta:
Resveratrol as a nontoxic excipient stabilizes insulin in a bioactive hexameric form. 915-927
Volume 34, Number 9, September 2020
- Dagmar Stumpfe, Huabin Hu, Jürgen Bajorath
Advances in exploring activity cliffs. 929-942 - Huabin Hu, Jürgen Bajorath
Simplified activity cliff network representations with high interpretability and immediate access to SAR information. 943-952 - Matthew P. Baumgartner
Side chain virtual screening of matched molecular pairs: a PDB-wide and ChEMBL-wide analysis. 953-963 - Metin Yazar
Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy. 965-974 - Izudin Redzepovic
Predictive potential of eigenvalue-based topological molecular descriptors. 975-982 - Manish Kumar Tripathi
Computational exploration and experimental validation to identify a dual inhibitor of cholinesterase and amyloid-beta for the treatment of Alzheimer's disease. 983-1002 - Shalini Thiruchittampalam
Plausible compounds drawn from plants as curative agents for neurodegeneration: An in-silico approach. 1003-1011
Volume 34, Number 10, October 2020
- Raquel Rodríguez-Pérez
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. 1013-1026 - Gregory L. Szwabowski
Benchmarking GPCR homology model template selection in combination with de novo loop generation. 1027-1044 - Almudena Perona
Distinct binding of cetirizine enantiomers to human serum albumin and the human histamine receptor H1. 1045-1062 - Isaias Lans, Karen Palacio-Rodríguez
Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles. 1063-1077 - José L. Borioni
An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme. 1079-1090 - Natalie M. Colodette, Lucas S. Franco, Rodolfo C. Maia, Harold H. Fokoue, Carlos Mauricio R. Sant'Anna
Novel phosphatidylinositol 4-kinases III beta (PI4KIIIβ) inhibitors discovered by virtual screening using free energy models. 1091-1103 - Phasit Charoenkwan, Chanin Nantasenamat
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. 1105-1116
Volume 34, Number 11, November 2020
- Robert D. Clark
Design and tests of prospective property predictions for novel antimalarial 2-aminopropylaminoquinolones. 1117-1132 - Mei Qian Yau
Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset. 1133-1145 - Ammar Abdo
Monomer structure fingerprints: an extension of the monomer composition version for peptide databases. 1147-1156 - Denis B. Tikhonov
Phenylalkylamines in calcium channels: computational analysis of experimental structures. 1157-1169 - Marianna Bufano
Modeling Epac1 interactions with the allosteric inhibitor AM-001 by co-solvent molecular dynamics. 1171-1179 - Silvia Atanasio, Giuseppe Deganutti
Addressing free fatty acid receptor 1 (FFAR1) activation using supervised molecular dynamics. 1181-1193 - Kai Liu
Prediction of ligand binding mode among multiple cross-docking poses by molecular dynamics simulations. 1195-1205
Volume 34, Number 12, December 2020
- Dimitar G. Yonchev
DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology. 1207-1218 - Brian Olson
An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets. 1219-1228 - Mst. Shamima Khatun, Md. Mehedi Hasan
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations. 1229-1236 - Sara Sarfaraz
Combining fragment docking with graph theory to improve ligand docking for homology model structures. 1237-1259 - Hossein Aghazadeh
Interactions of GF-17 derived from LL-37 antimicrobial peptide with bacterial membranes: a molecular dynamics simulation study. 1261-1273 - Julián A. Bélgamo, Lucas N. Alberca, Jorge L. Pórfido
Application of target repositioning and in silico screening to exploit fatty acid binding proteins (FABPs) from Echinococcus multilocularis as possible drug targets. 1275-1288 - Jianzhong Chen
Mutation-mediated influences on binding of anaplastic lymphoma kinase to crizotinib decoded by multiple replica Gaussian accelerated molecular dynamics. 1289-1305
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