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Outlook on the Development and Application of Molecular Simulations in Latin America
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Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2020, 60, 2, 435–438
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https://doi.org/10.1021/acs.jcim.0c00112
Published February 2, 2020

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SPECIAL ISSUE

This article is part of the Molecular Simulation in Latin America: Coming of Age special issue.

This special issue dedicated to Molecular Simulation in Latin America covers a wide range of topics, from new algorithms and software to various applications of computational chemistry and chemoinformatics, to describe the complexity of molecular systems. It provides a relief map of the Latin American research landscape in the field of molecular simulation as gathered from a total of 60 accepted manuscripts. A few other contributions will appear later on in regular JCIM issues due to time constraints.

This issue contributions come from eight different countries Argentina (8), Brazil (33), Chile (8), Colombia (2), Cuba (2), Equator (1), Mexico (4), and Uruguay (1). About 11.55% of the contributions have institutional addresses in more than in one country. Non-Latin American co-authorships are near exclusively from the European Union (13.3%) and Anglo America (8.3%). A total of 16.7% of the publications of this issue are joint contributions from institutions in more than one Latin American country. Argentina and Chile share ca. 33.3% and 42.8% of their contributions with coauthors based in Latin America. In contrast, only 9.1% of Brazilian contributions are joint publications with Latin American researchers. The traditionally low regional integration is a persistent trend in virtually all science fields, including computational chemistry. (1)

The contributions to the special issue on Molecular Simulation in Latin America can be assembled in a handful of topics. Five new computational tools have been developed by the Latin America’s molecular simulation community: SuAVE, (2) GEMS-Pack, (3) BitClust, (4) an implementation of QT algorithm, (5) and MuLiMs-MCoMPAs. (6) From an algorithm to compute 3D-protein structural descriptors and a general graphical interface for the setup of molecular dynamics simulation to a memory-efficient protocol and a new method to calculate local curvature of soft matter, these algorithms and software will be useful additions to the presently available computational toolkits in molecular simulation.

MD simulations proved to be one of the most popular computational chemistry tools. In this special issue, (1) atomistic MD simulations have been applied in many studies to understand key structural and functional properties of proteins, (7−11) receptor activation, (12) insights into the binding of ligands, (13−18) mutations, (16,19−21) protein folding, (22) and inhibitor design. (23) In addition, combined free energy simulations and NMR chemical-shift perturbation has been utilized to identify transient cation-π contacts in proteins. (24) The method has also been applied in determining specific ion effect of zwitterionic micelle, (25) conformational sampling of carbohydrates (26) and lipid-linked oligosaccharides, (27) time-resolved fluorescence anisotropy measurement, (28) understanding the effect of chemical modification of carbon nanohorn (CNH) as a novel nanovector for drug delivery system, (29) Na+-ion and K+-ion transport features in the polymer-ionic liquid, (30) gas adsorption in imidazolium based ionic liquids, (31) ethanol oxidation in O2/N2 and O2/CO2 environments at high temperatures. (32) Coarse grained (CG) models have now becomes utility in simulating large-scale biomolecular processes on time scales inaccessible to all-atom models. In this special issue, it has been utilized in understanding viral capsid disassembly, (33) post-translational modifications, (34) lipid self-assembly into micelles, bicelles, and reverse micelles, (35) RNA, (36) and the lipophilicity of cholesterol. (60) Moving from deterministic to stochastic sampling algorithms, MC simulations have shown the importance of using periodic conditions in simulations of drug adsorption to ZIF-8 nanocrystals. (37) This method has also been used to understand the antigen–antibody interactions that leads to identification of electrostatic epitopes in the nonstructural viral protein 1 (NS1) of the West Nile and Zika viruses. (38)

Computational simulations have been applied across different spatial resolutions. High level computational methods at the semiempirical and DFT levels have been used to evaluate the performance of the linear response within the elimination of the small component (LRESC) formalism on top of some DFT functionals to compute tin shielding constants in SnX4 (X = H, F, Cl, Br, I) (39) and to examine the contribution of different tautomeric forms of doxorubicin to its absorption spectra (40) as well as the adsorption of CO2 on clusters of transition metals (41) and the importance of accounting for atomic structure in the modeling of field emission enhancement factors for small single-walled carbon nanotubes (SWCNTs). (42) A new approach to obtain reactivity descriptors for enzymatic catalysis from its reaction profile determined via semiempirical methods has also been presented. (43) Hybrid QM/MM methods are a robust approach to describe enzymatic reactions where conformational changes must be taken into account. (44) In this issue, QM/MM MD simulations have been applied to investigate the reaction mechanism of thiol overoxidation in peroxiredoxins, (45) carboligation in acetohydroxy synthase, (46) chloride selectivity and halogenase regioselectivity of the SalL enzyme, (47) hydrolysis of iron–sulfur clusters, (48) and to calculate free energy profiles used as predictors of affinity for reversible, covalent inhibitors of rhodesain. (49)

Drug discovery has benefited immensely with the advanced of computer simulation’s methodology. Virtual screening in preliminary stage of computer aided drug design has become a popular approach in the searching of potential lead compound from a huge database. Today, there have been many protein–ligand docking programs available coupled with increasing computing power, which have enabled virtual screening of large databases to expedite the discovery of many potential inhibitors. Such studies that appear in this special issue include the identification of hydroxamic acids as human ecto-5′-nucleotidase inhibitors, (50) cruzain inhibitors with trypanocidal activity (51) as well as new (52) and repurposed (61) Zika virus proteases and methyltransferase inhibitors, (49) and the design of novel peptides that bind to neuroligin-1 for synaptic targeting. (53) Molecular docking predictions are entirely dependent on the accuracy of the algorithms and scoring functions. The evaluation of the performance is pertinent in the development of highly accurate docking methods that can overcome the limitation of the existing ones. This is highlighted in the benchmarking of the DockThor Program on the LEADS-PEP protein-peptide data set. (54) Last but not least, this special issue also reports on studies involving the application of machine learning in drug discovery (55) and material chemistry. (56,57) Schleder et al. (56) presented an analysis of the machine learning and data mining techniques for the prediction of molecular and materials properties. Additionally, a database of nuclear independent chemical shifts in fused aromatic rings have been proposed with potential uses in data science and machine learning. (58)

This special issue also brings a perspective from an early career computational scientist on the challenges faced (59) in addition to the outlook on current advances of the development and application of molecular simulations for the Latin American community noted above. Latin America and the Caribbean have 8.6% of the world population but only 2.5% of the world’s scientists. (62) There are 261 researchers per million population with the highest rates in Argentina (715), Brazil (315), and in Mexico (217). For comparison, these numbers are 633 in China, 2982 in France, 3209 in Germany, 4374 in the USA, and 5085 in Japan. Of course, it goes without saying that an increase in the number of high-quality publications from Latin America requires a greater commitment to the steady increase in gross domestic expenditure on R&D. (62) In 2016, the five countries reporting the highest R&D investments in absolute numbers were (in billion PPP dollars): United States (511), China (452), Japan (166), Germany (119), and the Republic of Korea (78). For comparison, the United States spent 2.7% of GDP on R&D compared to Brazil (1.3%), Argentina (0.6%), Costa Rica (0.6%), and Mexico (0.5%), the top performers in Latin America. While global research expenditure grew faster (+30.5%) than the global economy (+20.1%) between 2007 and 2013, Latin America’s global share of spending rose only slightly, from 3.1% to 3.4% in the same period.

It is likely that low gross domestic expenditure on R&D will remain a hurdle to be overcome by Latin American researchers in this new decade due to the latest political-economic and social instabilities in the region. Nonetheless, we are delighted that the Latin American molecular simulation community is pushing forward to a stronger participation in JCIM. As this special issue upholds, Molecular Simulation in Latin America has come of age!

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References

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This article references 62 other publications.

  1. 1
    Soares, T. A.; Wahab, H. A. Molecular Simulation in Latin America: Coming of Age. J. Chem. Inf. Model. 2019, 59, 3601– 3602,  DOI: 10.1021/acs.jcim.9b00589
    Google Scholar
    1
    Molecular Simulation in Latin America: Coming of Age
    Soares, Thereza A.; Wahab, Habibah A.
    Journal of Chemical Information and Modeling (2019), 59 (9), 3601-3602CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    There is no expanded citation for this reference.
  2. 2
    Santos, D. E. S.; Pontes, F. J. S.; Lins, R. D.; Coutinho, K.; Soares, T. A. Suave: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00569
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Silva, L. A.; Correia, J. C. G. Gems-Pack: A Graphical User Interface for the Packmol Program. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00740
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    González-Alemán, R.; Hernández-Castillo, D.; Rodríguez-Serradet, A.; Caballero, J.; Hernández-Rodríguez, E. W.; Montero-Cabrera, L. Bitclust: Fast Geometrical Clustering of Long Molecular Dynamics Simulations. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00828
    Google Scholar
    There is no corresponding record for this reference.
  5. 5
    González-Alemán, R.; Hernández-Castillo, D.; Caballero, J.; Montero-Cabrera, L. A. Quality Threshold Clustering of Molecular Dynamics: A Word of Caution. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00558
    Google Scholar
    There is no corresponding record for this reference.
  6. 6
    Contreras-Torres, E.; Marrero-Ponce, Y.; Terán, J. E.; García-Jacas, C. R.; Brizuela, C. A.; Sánchez-Rodríguez, J. C. Mulims-Mcompas: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00629
    Google Scholar
    There is no corresponding record for this reference.
  7. 7
    Querino Lima Afonso, M.; da Fonseca, N. J.; de Oliveira, L. C.; Lobo, F. P.; Bleicher, L. Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00903
    Google Scholar
    There is no corresponding record for this reference.
  8. 8
    Mendoza, F.; Jaña, G. A. Unveiling the Dynamical and Structural Features That Determine the Orientation of the Acceptor Substrate in the Landomycin Glycosyltransferase Langt2 and Its Variant with C-Glycosylation Activity. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00865
    Google Scholar
    There is no corresponding record for this reference.
  9. 9
    Alves, N. R. C.; Pecci, A.; Alvarez, L. D. Structural Insights into the Ligand Binding Domain of the Glucocorticoid Receptor: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00776
    Google Scholar
    There is no corresponding record for this reference.
  10. 10
    Santiago, Á.; Razo-Hernández, R. S.; Pastor, N. Revealing the Structural Contributions to Thermal Adaptation of the Tata-Box Binding Protein: Molecular Dynamics and Qspr Analyses. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00824
    Google Scholar
    There is no corresponding record for this reference.
  11. 11
    Solorza, J.; Recabarren, R.; Alzate-Morales, J. Molecular Insights into the Trapping Effect of Ca2+ in Protein Kinase A: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00857
    Google Scholar
    There is no corresponding record for this reference.
  12. 12
    Lopez, E. D.; Burastero, O.; Arcon, J. P.; Defelipe, L. A.; Ahn, N. G.; Marti, M. A.; Turjanski, A. G. Kinase Activation by Small Conformational Changes. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00782
    Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Rossino, G.; Orellana, I.; Caballero, J.; Schepmann, D.; Wünsch, B.; Rui, M.; Rossi, D.; González-Avendaño, M.; Collina, S.; Vergara-Jaque, A. New Insights into the Opening of the Occluded Ligand-Binding Pocket of Sigma1 Receptor: Binding of a Novel Bivalent Rc-33 Derivative. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00649
    Google Scholar
    There is no corresponding record for this reference.
  14. 14
    Racigh, V.; Ormazábal, A.; Palma, J.; Pierdominici-Sottile, G. Positively Charged Residues in the Head Domain of P2 × 4 Receptors Assist the Binding of Atp. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00856
    Google Scholar
    There is no corresponding record for this reference.
  15. 15
    Lima Neto, J. X.; Bezerra, K. S.; Barbosa, E. D.; Oliveira, J. I. N.; Manzoni, V.; Soares-Rachetti, V. P.; Albuquerque, E. L.; Fulco, U. L. Exploring the Binding Mechanism of Gabab Receptor Agonists and Antagonists through in Silico Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01025
    Google Scholar
    There is no corresponding record for this reference.
  16. 16
    Alviz-Amador, A.; Galindo-Murillo, R.; Pérez-González, H.; Rodríguez-Cavallo, E.; Vivas-Reyes, R.; Méndez-Cuadro, D. Effect of 4-Hne Modification on Zu5-Ank Domain and the Formation of Their Complex with Β-Spectrin: A Molecular Dynamics Simulation Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00772
    Google Scholar
    There is no corresponding record for this reference.
  17. 17
    Prudkin-Silva, C.; Pérez, O. E.; Martínez, K. D.; Barroso da Silva, F. L. Combined Experimental and Molecular Simulation Study of Insulin–Chitosan Complexation Driven by Electrostatic Interactions. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00814
    Google Scholar
    There is no corresponding record for this reference.
  18. 18
    Neves Cruz, J.; Santana de Oliveira, M.; Gomes Silva, S.; Pedro da Silva Souza Filho, A.; Santiago Pereira, D.; Lima e Lima, A. H.; de Aguiar Andrade, E. H. Insight into the Interaction Mechanism of Nicotine, Nnk, and Nnn with Cytochrome P450 2a13 Based on Molecular Dynamics Simulation. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00741
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Aguayo-Ortiz, R.; Dominguez, L. Effects of Mutating Trp42 Residue on Γd-Crystallin Stability. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00747
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Acosta Tapia, N.; Galindo, J. F.; Baldiris, R. Insights into the Effect of Lowe Syndrome-Causing Mutation P.Asn591lys of Ocrl-1 through Protein-Protein Interaction Networks and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01077
    Google Scholar
    There is no corresponding record for this reference.
  21. 21
    Santini, B. L.; Zúñiga-Bustos, M.; Vidal-Limon, A.; Alderete, J. B.; Águila, S. A.; Jiménez, V. A. In Silico Design of Novel Mutant Anti-Muc1 Aptamers for Targeted Cancer Therapy. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00756
    Google Scholar
    There is no corresponding record for this reference.
  22. 22
    Ferreira, P. H. B.; Freitas, F. C.; McCully, M. E.; Slade, G. G.; de Oliveira, R. J. The Role of Electrostatics and Folding Kinetics on the Thermostability of Homologous Cold Shock Proteins. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00797
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    B. da Silva, F.; M. de Oliveira, V.; Sanches, M. N.; Contessoto, V. G.; Leite, V. B. P. Rational Design of Chymotrypsin Inhibitor 2 by Optimizing Non-Native Interactions. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00911
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Reis, A. A. O.; Sayegh, R. S. R.; Marana, S. R.; Arantes, G. M. Combining Free Energy Simulations and NMR Chemical-Shift Perturbation to Identify Transient Cation−Π Contacts in Proteins. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00859
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Mortara, L.; Chaimovich, H.; Cuccovia, I. M.; Horinek, D.; Lima, F. S. Dehydration Determines Hydrotropic Ion Affinity for Zwitterionic Micelles. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00870
    Google Scholar
    There is no corresponding record for this reference.
  26. 26
    de Meirelles, J. L.; Nepomuceno, F. C.; Peña-García, J.; Schmidt, R. R.; Pérez-Sánchez, H.; Verli, H. Current Status of Carbohydrates Information in the Protein Data Bank. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00874
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Arantes, P. R.; Pedebos, C.; Polêto, M. D.; Pol-Fachin, L.; Verli, H. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00904
    Google Scholar
    There is no corresponding record for this reference.
  28. 28
    Lopez, A. J.; Barros, E. P.; Martínez, L. On the Interpretation of Subtilisin Carlsberg Time-Resolved Fluorescence Anisotropy Decays: Modeling with Classical Simulations. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00539
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Almeida, E. R.; De Souza, L. A.; De Almeida, W. B.; Dos Santos, H. F. Chemically Modified Carbon Nanohorns as Nanovectors of the Cisplatin Drug: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00775
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    de Souza, R. M.; de Siqueira, L. J. A.; Karttunen, M.; Dias, L. G. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00750
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Neumann, J. G.; Stassen, H. Anion Effect on Gas Absorption in Imidazolium-Based Ionic Liquids. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00885
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Arvelos, S.; Hori, C. E. A Reaxff Study of Ethanol Oxidation in O2/N2 and O2/Co2 Environments at High Temperatures. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00886
    Google Scholar
    There is no corresponding record for this reference.
  33. 33
    Martínez, M.; Cooper, C. D.; Poma, A. B.; Guzman, H. V. Free Energies of the Disassembly of Viral Capsids from a Multiscale Molecular Simulation Approach. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00883
    Google Scholar
    There is no corresponding record for this reference.
  34. 34
    Garay, P. G.; Barrera, E. E.; Pantano, S. Post-Translational Modifications at the Coarse-Grained Level with the Sirah Force Field. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00900
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    de Souza, R. M.; Ratochinski, R. H.; Karttunen, M.; Dias, L. G. Self-Assembly of Phosphocholine Derivatives Using the Elba Coarse-Grained Model: Micelles, Bicelles, and Reverse Micelles. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00790
    Google Scholar
    There is no corresponding record for this reference.
  36. 36
    Poblete, S.; Pérez-Acle, T. Brief Comparison between Experimental and Computationally Generated Ensembles of Rna Dinucleotides. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00913
    Google Scholar
    There is no corresponding record for this reference.
  37. 37
    Proenza, Y. G.; Longo, R. L. Simulation of the Adsorption and Release of Large Drugs by Zif-8. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00893
    Google Scholar
    There is no corresponding record for this reference.
  38. 38
    Poveda-Cuevas, S. A.; Etchebest, C.; Barroso da Silva, F. L. Identification of Electrostatic Epitopes in Flavivirus by Computer Simulations: The Proceedpka Method. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00895
    Google Scholar
    There is no corresponding record for this reference.
  39. 39
    Melo, J. I.; Maldonado, A. F.; Aucar, G. A. Performance of the Lresc Model on Top of Dft Functionals for Relativistic NMR Shielding Calculations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00912
    Google Scholar
    There is no corresponding record for this reference.
  40. 40
    Florêncio e Silva, E.; Machado, E. S.; Vasconcelos, I. B.; Junior, S. A.; L. Dutra, J. D.; Freire, R. O.; da Costa, N. B. Are the Absorption Spectra of Doxorubicin Properly Described by Considering Different Tautomers?. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00785
    Google Scholar
    There is no corresponding record for this reference.
  41. 41
    Batista, K. E. A.; Ocampo-Restrepo, V. K.; Soares, M. D.; Quiles, M. G.; Piotrowski, M. J.; Da Silva, J. L. F. Ab Initio Investigation of Co2 Adsorption on 13-Atom 4d Clusters. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00792
    Google Scholar
    There is no corresponding record for this reference.
  42. 42
    de Castro, C. P.; de Assis, T. A.; Rivelino, R.; de B. Mota, F.; de Castilho, C. M. C.; Forbes, R. G. Modeling the Field Emission Enhancement Factor for Capped Carbon Nanotubes Using the Induced Electron Density. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00896
    Google Scholar
    There is no corresponding record for this reference.
  43. 43
    Grillo, I. B.; Urquiza-Carvalho, G. A.; Bachega, J. F. R.; Rocha, G. B. Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00860
    Google Scholar
    There is no corresponding record for this reference.
  44. 44
    Olivieri, F. A.; Burastero, O.; Drusin, S.; Defelipe, L. A.; Wetzler, D. E.; Turjanski, A. G.; Marti, M. A. Conformational and Reaction Dynamic Coupling in Histidine Kinases. Insights from Hybrid Qm/Mm Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00806
    Google Scholar
    There is no corresponding record for this reference.
  45. 45
    Semelak, J. A.; Battistini, F.; Radi, R.; Trujillo, M.; Zeida, A.; Estrin, D. A. Multiscale Modeling of Thiol Overoxidation in Peroxiredoxins by Hydrogen Peroxide. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00817
    Google Scholar
    There is no corresponding record for this reference.
  46. 46
    Mendoza, F.; Medina, F. E.; Jiménez, V. A.; Jaña, G. A. Catalytic Role of Gln202 in the Carboligation Reaction Mechanism of Yeast Ahas: A Qm/Mm Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00863
    Google Scholar
    There is no corresponding record for this reference.
  47. 47
    Pereira, P. R. M.; Araújo, J. d. O.; Silva, J. R. A.; Alves, C. N.; Lameira, J.; Lima, A. H. Exploring Chloride Selectivity and Halogenase Regioselectivity of the Sall Enzyme through Quantum Mechanical/Molecular Mechanical Modeling. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01079
    Google Scholar
    There is no corresponding record for this reference.
  48. 48
    Teixeira, M. H.; Curtolo, F.; Camilo, S. R. G.; Field, M. J.; Zheng, P.; Li, H.; Arantes, G. M. Modeling the Hydrolysis of Iron–Sulfur Clusters. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00881
    Google Scholar
    There is no corresponding record for this reference.
  49. 49
    da Costa, C. H. S.; Bonatto, V.; dos Santos, A. M.; Lameira, J.; Leitão, A.; Montanari, C. A. Evaluating Qm/Mm Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00847
    Google Scholar
    There is no corresponding record for this reference.
  50. 50
    Viviani, L. G.; Piccirillo, E.; Ulrich, H.; Amaral, A. T. d. Virtual Screening Approach for the Identification of Hydroxamic Acids as Novel Human Ecto-5′-Nucleotidase Inhibitors. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00884
    Google Scholar
    There is no corresponding record for this reference.
  51. 51
    de Souza, M. L.; de Oliveira Rezende Junior, C.; Ferreira, R. S.; Espinoza Chávez, R. M.; Ferreira, L. L. G.; Slafer, B. W.; Magalhães, L. G.; Krogh, R.; Oliva, G.; Cruz, F. C.; Dias, L. C.; Andricopulo, A. D. Discovery of Potent, Reversible, and Competitive Cruzain Inhibitors with Trypanocidal Activity: A Structure-Based Drug Design Approach. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00802
    Google Scholar
    There is no corresponding record for this reference.
  52. 52
    Santos, F. R. S.; Nunes, D. A. F.; Lima, W. G.; Davyt, D.; Santos, L. L.; Taranto, A. G.; M. S. Ferreira, J. Identification of Zika Virus Ns2b-Ns3 Protease Inhibitors by Structure-Based Virtual Screening and Drug Repurposing Approaches. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00933
    Google Scholar
    There is no corresponding record for this reference.
  53. 53
    Vásquez, P.; Vidal, F.; Torres, J.; Jiménez, V. A.; Guzmán, L. Rational Design and in Vitro Evaluation of Novel Peptides Binding to Neuroligin-1 for Synaptic Targeting. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01003
    Google Scholar
    There is no corresponding record for this reference.
  54. 54
    Santos, K. B.; Guedes, I. A.; Karl, A. L. M.; Dardenne, L. E. Highly Flexible Ligand Docking: Benchmarking of the Dockthor Program on the Leads-Pep Protein-Peptide Dataset. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00905
    Google Scholar
    There is no corresponding record for this reference.
  55. 55
    Fernández, A. Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00651
    Google Scholar
    There is no corresponding record for this reference.
  56. 56
    Schleder, G. R.; Padilha, A. C. M.; Reily Rocha, A.; Dalpian, G. M.; Fazzio, A. Ab Initio Simulations and Materials Chemistry in the Age of Big Data. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00781
    Google Scholar
    There is no corresponding record for this reference.
  57. 57
    Cravero, F.; Schustik, S. A.; Martínez, M. J.; Vázquez, G. E.; Díaz, M. F.; Ponzoni, I. Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the Fs4rvdd Algorithm Using Synthetic Polydisperse Data Sets. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00867
    Google Scholar
    There is no corresponding record for this reference.
  58. 58
    Alvarez-Ramírez, F.; Ruiz-Morales, Y. Database of Nuclear Independent Chemical Shifts (Nics) Versus Nicszz of Polycyclic Aromatic Hydrocarbons (Pahs). J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00909
    Google Scholar
    There is no corresponding record for this reference.
  59. 59
    Nunes-Alves, A. From Brazil to Germany: Challenges and Advantages. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00764
    Google Scholar
    There is no corresponding record for this reference.
  60. 60
    Fornasier, F.; de Souza, L. M. P.; Souza, F. R. d.; Reynaud, F.; Pimentel, A. S. The Lipophilicity of Coarse-Grained Cholesterol Models. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00830
    Google Scholar
    There is no corresponding record for this reference.
  61. 61
    Santos, F. R. d. S.; Lima, W. G.; Maia, E. H.; Assis, L. C.; Davyt, D.; Taranto, A. G.; Ferreira, J. M. S. Identification of a Potential Zika Virus Inhibitor Targeting NS5 Methyltransferase Using Virtual Screening and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00809
    Google Scholar
    There is no corresponding record for this reference.
  62. 62
    UNESCO. Director General. Unesco Science Report: Towards 2030; United Nations: 2015.
    Google Scholar
    There is no corresponding record for this reference.

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  1. Sergio Pantano, Luciana Capece, Laura Gagliardi, (Editor-in-Chief, JCTC)Kenneth M. Merz, Jr., (Editor-in-Chief, JCIM)Victor Batista, (Associate Editor, JCTC)Thereza A. Soares (Executive Editor, JCIM). Computational Chemistry in the Global South: A Latin American Perspective. Journal of Chemical Theory and Computation 2025, 21 (4) , 1507-1508. https://doi.org/10.1021/acs.jctc.5c00120
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    This article references 62 other publications.

    1. 1
      Soares, T. A.; Wahab, H. A. Molecular Simulation in Latin America: Coming of Age. J. Chem. Inf. Model. 2019, 59, 3601– 3602,  DOI: 10.1021/acs.jcim.9b00589
      1
      Molecular Simulation in Latin America: Coming of Age
      Soares, Thereza A.; Wahab, Habibah A.
      Journal of Chemical Information and Modeling (2019), 59 (9), 3601-3602CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      There is no expanded citation for this reference.
    2. 2
      Santos, D. E. S.; Pontes, F. J. S.; Lins, R. D.; Coutinho, K.; Soares, T. A. Suave: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00569
      There is no corresponding record for this reference.
    3. 3
      Silva, L. A.; Correia, J. C. G. Gems-Pack: A Graphical User Interface for the Packmol Program. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00740
      There is no corresponding record for this reference.
    4. 4
      González-Alemán, R.; Hernández-Castillo, D.; Rodríguez-Serradet, A.; Caballero, J.; Hernández-Rodríguez, E. W.; Montero-Cabrera, L. Bitclust: Fast Geometrical Clustering of Long Molecular Dynamics Simulations. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00828
      There is no corresponding record for this reference.
    5. 5
      González-Alemán, R.; Hernández-Castillo, D.; Caballero, J.; Montero-Cabrera, L. A. Quality Threshold Clustering of Molecular Dynamics: A Word of Caution. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00558
      There is no corresponding record for this reference.
    6. 6
      Contreras-Torres, E.; Marrero-Ponce, Y.; Terán, J. E.; García-Jacas, C. R.; Brizuela, C. A.; Sánchez-Rodríguez, J. C. Mulims-Mcompas: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00629
      There is no corresponding record for this reference.
    7. 7
      Querino Lima Afonso, M.; da Fonseca, N. J.; de Oliveira, L. C.; Lobo, F. P.; Bleicher, L. Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00903
      There is no corresponding record for this reference.
    8. 8
      Mendoza, F.; Jaña, G. A. Unveiling the Dynamical and Structural Features That Determine the Orientation of the Acceptor Substrate in the Landomycin Glycosyltransferase Langt2 and Its Variant with C-Glycosylation Activity. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00865
      There is no corresponding record for this reference.
    9. 9
      Alves, N. R. C.; Pecci, A.; Alvarez, L. D. Structural Insights into the Ligand Binding Domain of the Glucocorticoid Receptor: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00776
      There is no corresponding record for this reference.
    10. 10
      Santiago, Á.; Razo-Hernández, R. S.; Pastor, N. Revealing the Structural Contributions to Thermal Adaptation of the Tata-Box Binding Protein: Molecular Dynamics and Qspr Analyses. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00824
      There is no corresponding record for this reference.
    11. 11
      Solorza, J.; Recabarren, R.; Alzate-Morales, J. Molecular Insights into the Trapping Effect of Ca2+ in Protein Kinase A: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00857
      There is no corresponding record for this reference.
    12. 12
      Lopez, E. D.; Burastero, O.; Arcon, J. P.; Defelipe, L. A.; Ahn, N. G.; Marti, M. A.; Turjanski, A. G. Kinase Activation by Small Conformational Changes. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00782
      There is no corresponding record for this reference.
    13. 13
      Rossino, G.; Orellana, I.; Caballero, J.; Schepmann, D.; Wünsch, B.; Rui, M.; Rossi, D.; González-Avendaño, M.; Collina, S.; Vergara-Jaque, A. New Insights into the Opening of the Occluded Ligand-Binding Pocket of Sigma1 Receptor: Binding of a Novel Bivalent Rc-33 Derivative. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00649
      There is no corresponding record for this reference.
    14. 14
      Racigh, V.; Ormazábal, A.; Palma, J.; Pierdominici-Sottile, G. Positively Charged Residues in the Head Domain of P2 × 4 Receptors Assist the Binding of Atp. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00856
      There is no corresponding record for this reference.
    15. 15
      Lima Neto, J. X.; Bezerra, K. S.; Barbosa, E. D.; Oliveira, J. I. N.; Manzoni, V.; Soares-Rachetti, V. P.; Albuquerque, E. L.; Fulco, U. L. Exploring the Binding Mechanism of Gabab Receptor Agonists and Antagonists through in Silico Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01025
      There is no corresponding record for this reference.
    16. 16
      Alviz-Amador, A.; Galindo-Murillo, R.; Pérez-González, H.; Rodríguez-Cavallo, E.; Vivas-Reyes, R.; Méndez-Cuadro, D. Effect of 4-Hne Modification on Zu5-Ank Domain and the Formation of Their Complex with Β-Spectrin: A Molecular Dynamics Simulation Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00772
      There is no corresponding record for this reference.
    17. 17
      Prudkin-Silva, C.; Pérez, O. E.; Martínez, K. D.; Barroso da Silva, F. L. Combined Experimental and Molecular Simulation Study of Insulin–Chitosan Complexation Driven by Electrostatic Interactions. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00814
      There is no corresponding record for this reference.
    18. 18
      Neves Cruz, J.; Santana de Oliveira, M.; Gomes Silva, S.; Pedro da Silva Souza Filho, A.; Santiago Pereira, D.; Lima e Lima, A. H.; de Aguiar Andrade, E. H. Insight into the Interaction Mechanism of Nicotine, Nnk, and Nnn with Cytochrome P450 2a13 Based on Molecular Dynamics Simulation. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00741
      There is no corresponding record for this reference.
    19. 19
      Aguayo-Ortiz, R.; Dominguez, L. Effects of Mutating Trp42 Residue on Γd-Crystallin Stability. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00747
      There is no corresponding record for this reference.
    20. 20
      Acosta Tapia, N.; Galindo, J. F.; Baldiris, R. Insights into the Effect of Lowe Syndrome-Causing Mutation P.Asn591lys of Ocrl-1 through Protein-Protein Interaction Networks and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01077
      There is no corresponding record for this reference.
    21. 21
      Santini, B. L.; Zúñiga-Bustos, M.; Vidal-Limon, A.; Alderete, J. B.; Águila, S. A.; Jiménez, V. A. In Silico Design of Novel Mutant Anti-Muc1 Aptamers for Targeted Cancer Therapy. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00756
      There is no corresponding record for this reference.
    22. 22
      Ferreira, P. H. B.; Freitas, F. C.; McCully, M. E.; Slade, G. G.; de Oliveira, R. J. The Role of Electrostatics and Folding Kinetics on the Thermostability of Homologous Cold Shock Proteins. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00797
      There is no corresponding record for this reference.
    23. 23
      B. da Silva, F.; M. de Oliveira, V.; Sanches, M. N.; Contessoto, V. G.; Leite, V. B. P. Rational Design of Chymotrypsin Inhibitor 2 by Optimizing Non-Native Interactions. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00911
      There is no corresponding record for this reference.
    24. 24
      Reis, A. A. O.; Sayegh, R. S. R.; Marana, S. R.; Arantes, G. M. Combining Free Energy Simulations and NMR Chemical-Shift Perturbation to Identify Transient Cation−Π Contacts in Proteins. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00859
      There is no corresponding record for this reference.
    25. 25
      Mortara, L.; Chaimovich, H.; Cuccovia, I. M.; Horinek, D.; Lima, F. S. Dehydration Determines Hydrotropic Ion Affinity for Zwitterionic Micelles. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00870
      There is no corresponding record for this reference.
    26. 26
      de Meirelles, J. L.; Nepomuceno, F. C.; Peña-García, J.; Schmidt, R. R.; Pérez-Sánchez, H.; Verli, H. Current Status of Carbohydrates Information in the Protein Data Bank. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00874
      There is no corresponding record for this reference.
    27. 27
      Arantes, P. R.; Pedebos, C.; Polêto, M. D.; Pol-Fachin, L.; Verli, H. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00904
      There is no corresponding record for this reference.
    28. 28
      Lopez, A. J.; Barros, E. P.; Martínez, L. On the Interpretation of Subtilisin Carlsberg Time-Resolved Fluorescence Anisotropy Decays: Modeling with Classical Simulations. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00539
      There is no corresponding record for this reference.
    29. 29
      Almeida, E. R.; De Souza, L. A.; De Almeida, W. B.; Dos Santos, H. F. Chemically Modified Carbon Nanohorns as Nanovectors of the Cisplatin Drug: A Molecular Dynamics Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00775
      There is no corresponding record for this reference.
    30. 30
      de Souza, R. M.; de Siqueira, L. J. A.; Karttunen, M.; Dias, L. G. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00750
      There is no corresponding record for this reference.
    31. 31
      Neumann, J. G.; Stassen, H. Anion Effect on Gas Absorption in Imidazolium-Based Ionic Liquids. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00885
      There is no corresponding record for this reference.
    32. 32
      Arvelos, S.; Hori, C. E. A Reaxff Study of Ethanol Oxidation in O2/N2 and O2/Co2 Environments at High Temperatures. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00886
      There is no corresponding record for this reference.
    33. 33
      Martínez, M.; Cooper, C. D.; Poma, A. B.; Guzman, H. V. Free Energies of the Disassembly of Viral Capsids from a Multiscale Molecular Simulation Approach. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00883
      There is no corresponding record for this reference.
    34. 34
      Garay, P. G.; Barrera, E. E.; Pantano, S. Post-Translational Modifications at the Coarse-Grained Level with the Sirah Force Field. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00900
      There is no corresponding record for this reference.
    35. 35
      de Souza, R. M.; Ratochinski, R. H.; Karttunen, M.; Dias, L. G. Self-Assembly of Phosphocholine Derivatives Using the Elba Coarse-Grained Model: Micelles, Bicelles, and Reverse Micelles. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00790
      There is no corresponding record for this reference.
    36. 36
      Poblete, S.; Pérez-Acle, T. Brief Comparison between Experimental and Computationally Generated Ensembles of Rna Dinucleotides. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00913
      There is no corresponding record for this reference.
    37. 37
      Proenza, Y. G.; Longo, R. L. Simulation of the Adsorption and Release of Large Drugs by Zif-8. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00893
      There is no corresponding record for this reference.
    38. 38
      Poveda-Cuevas, S. A.; Etchebest, C.; Barroso da Silva, F. L. Identification of Electrostatic Epitopes in Flavivirus by Computer Simulations: The Proceedpka Method. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00895
      There is no corresponding record for this reference.
    39. 39
      Melo, J. I.; Maldonado, A. F.; Aucar, G. A. Performance of the Lresc Model on Top of Dft Functionals for Relativistic NMR Shielding Calculations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00912
      There is no corresponding record for this reference.
    40. 40
      Florêncio e Silva, E.; Machado, E. S.; Vasconcelos, I. B.; Junior, S. A.; L. Dutra, J. D.; Freire, R. O.; da Costa, N. B. Are the Absorption Spectra of Doxorubicin Properly Described by Considering Different Tautomers?. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00785
      There is no corresponding record for this reference.
    41. 41
      Batista, K. E. A.; Ocampo-Restrepo, V. K.; Soares, M. D.; Quiles, M. G.; Piotrowski, M. J.; Da Silva, J. L. F. Ab Initio Investigation of Co2 Adsorption on 13-Atom 4d Clusters. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00792
      There is no corresponding record for this reference.
    42. 42
      de Castro, C. P.; de Assis, T. A.; Rivelino, R.; de B. Mota, F.; de Castilho, C. M. C.; Forbes, R. G. Modeling the Field Emission Enhancement Factor for Capped Carbon Nanotubes Using the Induced Electron Density. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00896
      There is no corresponding record for this reference.
    43. 43
      Grillo, I. B.; Urquiza-Carvalho, G. A.; Bachega, J. F. R.; Rocha, G. B. Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00860
      There is no corresponding record for this reference.
    44. 44
      Olivieri, F. A.; Burastero, O.; Drusin, S.; Defelipe, L. A.; Wetzler, D. E.; Turjanski, A. G.; Marti, M. A. Conformational and Reaction Dynamic Coupling in Histidine Kinases. Insights from Hybrid Qm/Mm Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00806
      There is no corresponding record for this reference.
    45. 45
      Semelak, J. A.; Battistini, F.; Radi, R.; Trujillo, M.; Zeida, A.; Estrin, D. A. Multiscale Modeling of Thiol Overoxidation in Peroxiredoxins by Hydrogen Peroxide. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00817
      There is no corresponding record for this reference.
    46. 46
      Mendoza, F.; Medina, F. E.; Jiménez, V. A.; Jaña, G. A. Catalytic Role of Gln202 in the Carboligation Reaction Mechanism of Yeast Ahas: A Qm/Mm Study. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00863
      There is no corresponding record for this reference.
    47. 47
      Pereira, P. R. M.; Araújo, J. d. O.; Silva, J. R. A.; Alves, C. N.; Lameira, J.; Lima, A. H. Exploring Chloride Selectivity and Halogenase Regioselectivity of the Sall Enzyme through Quantum Mechanical/Molecular Mechanical Modeling. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01079
      There is no corresponding record for this reference.
    48. 48
      Teixeira, M. H.; Curtolo, F.; Camilo, S. R. G.; Field, M. J.; Zheng, P.; Li, H.; Arantes, G. M. Modeling the Hydrolysis of Iron–Sulfur Clusters. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00881
      There is no corresponding record for this reference.
    49. 49
      da Costa, C. H. S.; Bonatto, V.; dos Santos, A. M.; Lameira, J.; Leitão, A.; Montanari, C. A. Evaluating Qm/Mm Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00847
      There is no corresponding record for this reference.
    50. 50
      Viviani, L. G.; Piccirillo, E.; Ulrich, H.; Amaral, A. T. d. Virtual Screening Approach for the Identification of Hydroxamic Acids as Novel Human Ecto-5′-Nucleotidase Inhibitors. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00884
      There is no corresponding record for this reference.
    51. 51
      de Souza, M. L.; de Oliveira Rezende Junior, C.; Ferreira, R. S.; Espinoza Chávez, R. M.; Ferreira, L. L. G.; Slafer, B. W.; Magalhães, L. G.; Krogh, R.; Oliva, G.; Cruz, F. C.; Dias, L. C.; Andricopulo, A. D. Discovery of Potent, Reversible, and Competitive Cruzain Inhibitors with Trypanocidal Activity: A Structure-Based Drug Design Approach. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00802
      There is no corresponding record for this reference.
    52. 52
      Santos, F. R. S.; Nunes, D. A. F.; Lima, W. G.; Davyt, D.; Santos, L. L.; Taranto, A. G.; M. S. Ferreira, J. Identification of Zika Virus Ns2b-Ns3 Protease Inhibitors by Structure-Based Virtual Screening and Drug Repurposing Approaches. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00933
      There is no corresponding record for this reference.
    53. 53
      Vásquez, P.; Vidal, F.; Torres, J.; Jiménez, V. A.; Guzmán, L. Rational Design and in Vitro Evaluation of Novel Peptides Binding to Neuroligin-1 for Synaptic Targeting. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b01003
      There is no corresponding record for this reference.
    54. 54
      Santos, K. B.; Guedes, I. A.; Karl, A. L. M.; Dardenne, L. E. Highly Flexible Ligand Docking: Benchmarking of the Dockthor Program on the Leads-Pep Protein-Peptide Dataset. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00905
      There is no corresponding record for this reference.
    55. 55
      Fernández, A. Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00651
      There is no corresponding record for this reference.
    56. 56
      Schleder, G. R.; Padilha, A. C. M.; Reily Rocha, A.; Dalpian, G. M.; Fazzio, A. Ab Initio Simulations and Materials Chemistry in the Age of Big Data. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00781
      There is no corresponding record for this reference.
    57. 57
      Cravero, F.; Schustik, S. A.; Martínez, M. J.; Vázquez, G. E.; Díaz, M. F.; Ponzoni, I. Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the Fs4rvdd Algorithm Using Synthetic Polydisperse Data Sets. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00867
      There is no corresponding record for this reference.
    58. 58
      Alvarez-Ramírez, F.; Ruiz-Morales, Y. Database of Nuclear Independent Chemical Shifts (Nics) Versus Nicszz of Polycyclic Aromatic Hydrocarbons (Pahs). J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00909
      There is no corresponding record for this reference.
    59. 59
      Nunes-Alves, A. From Brazil to Germany: Challenges and Advantages. J. Chem. Inf. Model. 2019,  DOI: 10.1021/acs.jcim.9b00764
      There is no corresponding record for this reference.
    60. 60
      Fornasier, F.; de Souza, L. M. P.; Souza, F. R. d.; Reynaud, F.; Pimentel, A. S. The Lipophilicity of Coarse-Grained Cholesterol Models. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00830
      There is no corresponding record for this reference.
    61. 61
      Santos, F. R. d. S.; Lima, W. G.; Maia, E. H.; Assis, L. C.; Davyt, D.; Taranto, A. G.; Ferreira, J. M. S. Identification of a Potential Zika Virus Inhibitor Targeting NS5 Methyltransferase Using Virtual Screening and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2020,  DOI: 10.1021/acs.jcim.9b00809
      There is no corresponding record for this reference.
    62. 62
      UNESCO. Director General. Unesco Science Report: Towards 2030; United Nations: 2015.
      There is no corresponding record for this reference.