Publicado
Docking study of Abexinostat and Acalabrutinib chemotherapeutic anti-cancer agents for non-Hodgkin lymphoma, including mantle cell lymphoma, chronic lymphocytic leukemia/small lymphocytic as well as for B-cell lymphoma
Estudio de acoplamiento molecular de los agentes quimioterapéuticos anticancerígenos Abexinostat y Acalabrutinib para el linfoma no Hodgkin, incluyendo el linfoma de células del manto, la leucemia linfocítica crónica/linfoma linfocítico pequeño y el linfoma de células B
Estudo de acoplamento molecular dos agentes quimioterápicos antineoplásicos Abexinostat e Acalabrutinib para linfoma não Hodgkin, incluindo linfoma de células do manto, leucemia linfocítica crônica/linfoma de pequenas células e linfoma de células B
DOI:
https://doi.org/10.15446/rcciquifa.v55n3.126672Palabras clave:
Abexinostat, Acalabrutinib, Histone deacetylase (HDAC) inhibition, Bruton's tyrosine kinase (BTK) inhibition, Mantle cell lymphoma, Molecular docking, NMR spectroscopy, Targeted cancer therapy (en)Abexinostat, Acalabrutinib, inhibición de la histona deacetilasa (HDAC), inhibición de la tirosina quinasa de Bruton (BTK), linfoma de células del manto, acoplamiento molecular, espectroscopia de RMN, terapia dirigida contra el cáncer (es)
Abexinostat, Acalabrutinib, inibição de histona desacetilase (HDAC), inibição da tirosina quinase de Bruton (BTK), linfoma de células do manto, acoplamento molecular, espectroscopia de RMN, terapia direcionada contra o câncer (pt)
Descargas
Background: Targeted therapeutic strategies are critical for improving outcomes in lymphocytic malignancies, such as mantle cell lymphoma (MCL), where conventional chemotherapy often fails. Aim: This study presents a comprehensive structural and computational investigation of Abexinostat, a pan-histone deacetylase (HDAC) inhibitor, and Acalabrutinib, a selective Bruton's tyrosine kinase (BTK) inhibitor. Molecular integrity and functional group composition were confirmed using ¹H and ¹³C NMR spectroscopy, revealing the hydroxamic acid motif in Abexinostat and the hetero-aromatic core of Acalabrutinib, essential for target specificity. Methods: Ab initio geometry optimization and potential energy surface analyses demonstrated conformational stability and flexibility conducive to active site engagement. Molecular docking against multiple HDAC isoforms (1ZZ0, 2V5X, 3SFF, 4LY1) and BTK structures (6GL8, 6O8I) showed strong binding affinities, with Abexinostat forming bi-dentate chelation with Zn2+ and stabilizing hydrogen-bond and hydrophobic interactions, while Acalabrutinib covalently modified Cys481 and engaged key residues through van der Waals and hydrogen bonding interactions. Results: Notably, 3SFF and 2V5X complexes displayed the most favorable docking energies (−90.23 and −89.53 kcal/mol), highlighting isoform-specific susceptibility. These findings elucidate the molecular mechanisms underlying epigenetic modulation and B-cell receptor inhibition, supporting their complementary roles in lymphocytic cancer therapy. The integrated structural, computational, and docking analyses provide a rationale for potential combinatorial strategies that may enhance antitumor efficacy while minimizing off-target toxicity. Conclusions: This work lays a foundation for rational design of next-generation therapeutics targeting HDACs and BTK, advancing precision medicine approaches in hematological malignancies.
Antecedentes: Las estrategias terapéuticas dirigidas son cruciales para mejorar los resultados en las neoplasias linfocíticas, como el linfoma de células del manto (LCM), donde la quimioterapia convencional suele fracasar. Objetivo: Este estudio presenta una investigación estructural y computacional exhaustiva de Abexinostat, un inhibidor pan-histona deacetilasa (HDAC), y Acalabrutinib, un inhibidor selectivo de la tirosina quinasa de Bruton (BTK). La integridad molecular y la composición de los grupos funcionales se confirmaron mediante espectroscopia de RMN de ¹H y ¹³C, revelando el motivo de ácido hidroxámico en Abexinostat y el núcleo heteroaromático de Acalabrutinib, esenciales para la especificidad del objetivo. Métodos: La optimización geométrica ab initio y los análisis de la superficie de energía potencial demostraron estabilidad conformacional y flexibilidad propicias para la interacción con el sitio activo. El acoplamiento molecular contra múltiples isoformas de HDAC (1ZZ0, 2V5X, 3SFF, 4LY1) y estructuras de BTK (6GL8, 6O8I) mostraron fuertes afinidades de unión, con Abexinostat formando quelación bidentada con Zn2+ y estabilizando interacciones de enlace de hidrógeno e hidrofóbicas, mientras que Acalabrutinib modificó covalentemente Cys481 e interactuó con residuos clave a través de interacciones de van der Waals y enlaces de hidrógeno. Resultados: En particular, los complejos 3SFF y 2V5X mostraron las energías de acoplamiento más favorables (−90,23 y −89,53 kcal/mol), lo que resalta la susceptibilidad específica de la isoforma. Estos hallazgos dilucidan los mecanismos moleculares subyacentes a la modulación epigenética y la inhibición del receptor de células B, lo que respalda sus funciones complementarias en la terapia del cáncer linfocítico. Los análisis estructurales, computacionales y de acoplamiento molecular integrados proporcionan una base racional para posibles estrategias combinatorias que podrían mejorar la eficacia antitumoral y minimizar la toxicidad fuera de objetivo. Conclusiones: Este trabajo sienta las bases para el diseño racional de terapias de nueva generación dirigidas a las HDAC y la BTK, impulsando los enfoques de medicina de precisión en las neoplasias hematológicas.
Contexto: Estratégias terapêuticas direcionadas são cruciais para melhorar os resultados em neoplasias linfocíticas, como o linfoma de células do manto (LCM), onde a quimioterapia convencional frequentemente falha. Objetivo: Este estudo apresenta uma investigação estrutural e computacional abrangente do Abexinostat, um inibidor pan-histona desacetilase (HDAC), e do Acalabrutinib, um inibidor seletivo da tirosina quinase de Bruton (BTK). A integridade molecular e a composição dos grupos funcionais foram confirmadas por espectroscopia de RMN de ¹H e ¹³C, revelando o motivo do ácido hidroxâmico no Abexinostat e o núcleo heteroaromático do Acalabrutinib, essenciais para a especificidade do alvo. Métodos: A otimização geométrica ab initio e as análises da superfície de energia potencial demonstraram estabilidade conformacional e flexibilidade propícias à interação com o sítio ativo. O acoplamento molecular contra múltiplas isoformas de HDAC (1ZZ0, 2V5X, 3SFF, 4LY1) e estruturas de BTK (6GL8, 6O8I) mostrou fortes afinidades de ligação, com o Abexinostat formando quelação bidentada com Zn2+ e estabilizando interações hidrofóbicas e de ligação de hidrogênio, enquanto o Acalabrutinib modificou covalentemente a Cys481 e interagiu com resíduos-chave por meio de interações de van der Waals e ligações de hidrogênio. Resultados: Notavelmente, os complexos 3SFF e 2V5X exibiram as energias de acoplamento mais favoráveis (−90,23 e −89,53 kcal/mol), destacando a suscetibilidade específica da isoforma. Essas descobertas elucidam os mecanismos moleculares subjacentes à modulação epigenética e à inibição do receptor de células B, corroborando seus papéis complementares na terapia do câncer linfocítico. As análises integradas de estrutura, computação e acoplamento molecular fornecem uma base racional para potenciais estratégias combinatórias que podem aumentar a eficácia antitumoral, minimizando a toxicidade em alvos não específicos. Conclusões: Este trabalho estabelece as bases para o desenvolvimento racional de terapias de próxima geração direcionadas a HDACs e BTK, impulsionando abordagens de medicina de precisão em neoplasias hematológicas.
Referencias
1. S. Bhalla, S. Balasubramanian, K. David, M. Sirisawad, J. Buggy, L. Mauro, S. Prachand, R. Miller, L.I. Gordon & A.M. Evens. PCI-24781 induces caspase and reactive oxygen species-dependent apoptosis through NF-kappaB mechanisms and is synergistic with bortezomib in lymphoma cells. Clinical Cancer Research, 15(10), 3354–3365 (2009). https://doi.org/10.1158/1078-0432
2. N. Rivera-Del Valle, S. Gao, C.P. Miller, J. Fulbright, C. Gonzales, M. Sirisawad, S. Steggerda, J. Wheler, S. Balasubramanian & J. Chandra. PCI-24781, a novel hydroxamic acid HDAC inhibitor, exerts cytotoxicity and histone alterations via caspase-8 and FADD in leukemia cells. International Journal of Cell Biology, 2010, 207420 (2010). https://doi.org/10.1155/2010/207420
3. C. Yang, E. Choy, F.J. Hornicek, K.B. Wood, J.H. Schwab, X. Liu, H. Mankin & Z. Duan. Histone deacetylase inhibitor (HDACI) PCI-24781 potentiates cytotoxic effects of doxorubicin in bone sarcoma cells. Cancer Chemotherapy and Pharmacology, 67(2), 439–446 (2011). https://doi.org/10.1007/ s00280-010-1344-7
4. J.J. Buggy, Z.A. Cao, K.E. Bass, E. Verner, S. Balasubramanian, L. Liu, B.E. Schultz, P.R. Young & S.A. Dalrymple. CRA-024781: A novel synthetic inhibitor of histone deacetylase enzymes with antitumor activity in vitro and in vivo. Molecular Cancer Therapeutics, 5(5), 1309–1317 (2006). https://doi.org/10.1158/1535-7163.mct-05-0442
5. S. Adimoolam, M. Sirisawad, J. Chen, P. Thiemann, J.M. Ford & J.J. Buggy. HDAC inhibitor PCI-24781 decreases RAD51 expression and inhibits homologous recombination. Proceedings of the National Academy of Sciences of the United States of America, 104(49), 19482–19487 (2007). https:// doi.org/10.1073/pnas.0707828104
6. V. Ribrag, Vincent, W.S. Kim, R. Bouabdallah, S.T. Lim, B. Coiffier, Z. Felloussi, I. Kloos, Y. Luan, T. Graef & F. Morschhauser. Safety and efficacy of abexinostat, a pan-histone deacetylase (HDAC) inhibitor, in non-Hodgkin lymphoma and chronic lymphocytic leukemia: Results of an ongoing phase 2 study. Blood, 126(23), 256 (2015). https://doi.org/10.1182/blood.v126.23.256.256
7. M. Monajjemi, B. Honarparvar, H.H. Haeri & M. Heshmat. An ab initio quantum chemical investigation of solvenz-induced effect on 14N-NQR parameters of alanine, glycine, valine, and serine using a polarizable continuum model. Russian Journal of Physical Chemistry A, 80(1 Suppl.), S40–S44 (2006). https://doi.org/10.1134/s0036024406130073
8. F. Mollaamin & M. Monajjemi. Tailoring and functionalizing the graphitic-like GaN and GaP nanostructures as selective sensors for NO, NO2, and NH3 adsorbing: a DFT study. Journal of Molecular Modeling, 29(6), 170 (2023). https://doi.org/10.1007/s00894-023-05567-8
9. F. Mollaamin, S. Shahriari, M. Monajjemi & Z. Khalaj. Nanocluster of aluminum lattice via organic inhibitors coating: A study of Freundlich adsorption. Journal of Cluster Science, 34(3), 1547–1562 (2023). https://doi.org/10.1007/s10876-022-02335-1
10. H. Yahyaei & M. Monajjemi. Theoretical study of different solvent and temperature effects on doublewalled carbon nanotubes (DWNTs) and calixarene with amino acid: A QM/MM study. Fullerenes Nanotubes and Carbon Nanostructures, 22(4), 346–361 (2014). https://doi.org/10.1080/ 1536383x.2012.684190
11. M. Monajjemi & M. Ahmadianarog. Carbon nanotube as a deliver for sulforaphane in Broccoli vegetable in point of nuclear magnetic resonance and natural bond orbital specifications. Journal of Computational and Theoretical Nanoscience, 11(6), 1465–1471 (2014). https://doi.org/10.1166/jctn. 2014.3519
12. F. Mollaamin & M. Monajjemi. Transition metal (X = Mn, Fe, Co, Ni, Cu, Zn)-doped graphene as gas sensor for CO2 and NO2 detection: a molecular modeling framework by DFT perspective. Journal of Molecular Modeling, 29(4), 119 (2023). https://doi.org/10.1007/s00894-023-05526-3
13. H.S. Walter, S.A. Rule, M.J.S. Dyer, L. Karlin C, Jones, B. Cazin, et al. A phase 1 clinical trial of the selective BTK inhibitor ONO/GS-4059 in relapsed and refractory mature B-cell malignancies. Blood, 127(4), 411–419 (2016). https://doi.org/10.1182/blood-2015-08-664086
14. T.K. Nielsen, C. Hildmann, A. Dickmanns, A. Schwienhorst & R. Ficner. Crystal structure of a bacterial class 2 histone deacetylase homologue. Journal of Molecular Biology, 354, 107–120 (2005). https://doi.org/10.1016/j.jmb.2005.09.065
15. M. Monajjemi, H. Aghaie & F. Naderi. Thermodynamic study of interaction of TSPP, CoTsPc, and FeTsPc with calf thymus DNA. Biochemistry (Moscow), 72(6), 652–657 (2007). https://doi.org/ 10.1134/s0006297907060089
16. F. Mollaamin & M. Monajjemi. Molecular modelling framework of metal-organic clusters for conserving surfaces: Langmuir sorption through the TD-DFT/ONIOM approach. Molecular Simulation, 49(4), 365–376 (2023). https://doi.org/10.1080/08927022.2022.2159996
17. M. Monajjemi, M. Heshmata & H.H. Haeria. QM/MM model study on properties and structure of some antibiotics in gas phase: Comparison of energy and NMR chemical shift. Biochemistry (Moscow), 71, S113–S122 (2006). https://doi.org/10.1134/s0006297906130190.
18. A. Vannini, C. Volpari, P. Gallinari, P. Jones, M. Mattu, A. Carfi, R. Defrancesco, C. Steinkuhler & S. Di Marco. Substrate binding to histone deacetylases as revealed by crystal structure of HDAC8-substrate complex. EMBO Reports, 8, 879 (2007). https://doi.org/10.1038/sj.embor.7401047
19. B.E.L. Lauffer, R. Mintzer, R. Fong, S. Mukund, C. Tam, I. Zilberleyb, et al. Histone deacetylase (HDAC) inhibitor kinetic rate constants correlate with cellular histone acetylation but not transcription and cell viability. Journal of Biological Chemistry, 288(37), 26926–26943 (2013). https://doi.org/10.1074/jbc.M113.490706
20. M. Monajjemi, F. Naderi, F. Mollaamin & M. Khaleghian. Drug design outlook by calculation of second virial coefficient as a nano study. Journal of the Mexican Chemical Society, 56(2), 207–211 (2012). https://doi.org/10.29356/jmcs.v56i2.323
21. M. Monajjem, N. Karachi & F. Mollaamin. The investigation of sequence-dependent interaction of messenger RNA binding to carbon nanotube. Fullerenes, Nanotubes and Carbon Nanostructures, 22(7), 643–662 (2014). https://doi.org/10.1080/1536383x.2012.717557
22. L. Whitehead, M.R. Dobler, B. Radetich, Y. Zhu, P.W. Atadja, T. Claiborne, et al. Human HDAC isoform selectivity achieved via exploitation of the acetate release channel with structurally unique small molecule inhibitors. Bioorganic & Medicinal Chemistry, 19(15), 4626–4634 (2011). https://doi.org/10.1016/j.bmc.2011.06.030
23. P. Casara, J. Davidson, A. Claperon, G. Le Toumelin-Braizat, M. Vogler, A. Bruno, et al. S55746 is a novel orally active BCL-2 selective and potent inhibitor that impairs hematological tumor growth. Oncotarget, 9, 20075–20088 (2018). https://doi.org/10.18632/oncotarget.24744
24. S.H. Watterson, Q. Liu, M.B. Bertrand, D.G. Batt, L. Li, M.A. Pattoli, et al. Discovery of Branebrutinib (BMS-986195): A strategy for identifying a highly potent and selective covalent inhibitor providing rapid in vivo inactivation of Bruton's tyrosine kinase (BTK). Journal of Medicine Chemistry, 62, 3228–3250 (2019). https://doi.org/10.1021/acs.jmedchem.9b00167
25. M. Monajjemi. Quantum investigation of non-bonded interaction between the B15N15 ring, and BH2NBH2 (radical, cation, anion) systems: A nano molecularmotor. Structural Chemistry, 23(2), 551–580 (2012). https://doi.org/10.1007/s11224-011-9895-8
26. T. Zhang, G. Pramanik, K. Zhang, M. Gulka, L. Wang, J. Jing, F. Xu, Z. Li, Q. Wei, P. Cigler & Z. Chu. Toward quantitative bio-sensing with nitrogen-vacancy center in diamond. ACS Sensors, 6(6), 2077–2107 (2021). https://doi.org/10.1021/acssensors.1c00415
27. M. Monajjemi. Cell membrane causes the lipid bilayers to behave as variable capacitors: A resonance with self-induction of helical proteins. Biophysical Chemistry, 207, 114–127 (2015). https://doi.org/10.1016/j.bpc.2015.10.003.
28. F. Mollaamin, J. Najafpour, S. Ghadami, A.R. Ilkhani, M. Akrami & M. Monajjemi. The electromagnetic feature of B15N15Hx (x = 0, 4, 8, 12, 16, and 20) nano, rings: Quantum theory of atoms in molecules/NMR approach. Journal of Computational and Theoretical Nanoscience, 11(5), 1290–1298 (2014). https://doi.org/10.1166/jctn.2014.3495
29. M. Monajjemi & F. Mollaamin. Intermolecular simulation of nanobiological structures in point of potential energy and second virial coefficient. Journal of Computational and Theoretical Nanoscience, 9(12), 2208–2214 (2012). https://doi.org/10.1166/jctn.2012.2640
30. M. Monajjemi, S. Ketabi, M.H. Zadeh & A. Amiri. Simulation of DNA bases in water: Comparison of the Monte Carlo algorithm with molecular mechanics force fields. Biochemistry (Moscow), 71(Suppl. 1), S1–S8 (2006). https://doi.org/10.1134/s0006297906130013
31. F. Mollaamin & M. Monajjemi. Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method. Sensor Review, 43(4), 266–279 (2023). https://doi.org/10.1108/sr-03-2023-0040
32. M. Monajjemi, B. Honarparvar, S.M. Nasseri & M. Khaleghian. NQR and NMR study of hydrogen bonding interactions in anhydrous and monohydrated guanine cluster model: A computational study. Journal of Structural Chemistry, 50(1), 67–77 (2009). https://doi.org/10.1007/s10947-009-0009-z
33. B. Khalili-Hadad, F. Mollaamin & M. Monajjemi. Biophysical chemistry of macrocycles for drug delivery: A theoretical study. Russian Chemical Bulletin, 60(2), 238–241 (2011). https://doi.org/ 10.1007/s11172-011-0039-5
34. M. Monajjemi, B. Honarparvar, H.H. Haeri & M. Heshmat. An ab initio quantum chemical investigation of solvent-induced effect on 14N-NQR parameters of alanine, glycine, valine, and serine using a polarizable continuum model. Russian Journal of Physical Chemistry A, 80(1 Suppl.), S40–S44 (2006). https://doi.org/10.1134/s0036024406130073
35. M. Monajjemi, Majid, M.H. Razavian, F. Mollaamin, F. Naderi & B. Honarparvar. A theoretical thermochemical study of solute-solvent dielectric effects in the displacement of codon-anticodon base pairs. Russian Journal of Physical Chemistry A, 82(13), 2277–2285 (2008). https://doi.org/ 10.1134/s0036024408130207
36. H. Yahyaei, M. Monajjemi, H. Aghaie & K. Zare. Monte Carlo quantum calculation for double-walled carbon nanotubes, (DWNTs) combined to calixarene [6]. Journal of Computational and Theoretical Nanoscience, 10(10), 2332–2341 (2013). https://doi.org/10.1166/jctn.2013.3210
37. F. Mollaamin & M. Monajjemi. Doping of graphene nanostructure with iron, nickel and zinc as selective detector for the toxic gas removal: A density functional theory study. C: Journal of Carbon Research, 9(1), 20 (2023), https://doi.org/10.3390/c9010020
38. M. Monajjemi, F. Mollaamin, M. Gholami, S. Sadrnezhad & H. Passdar. Quantum chemical parameters of some organic corrosion inhibitors, pyridine, 2-picoline 4-picoline and 2,4-lutidine, adsorption at aluminum surface in hydrocholoric and nitric acids and comparison between two acidic media. Main Group Metal Chemistry, 26(6), 349–361 (2003). URL: https://www. degruyterbrill.com/document/doi/10.1515/MGMC.2003.26.6.349/html?utm_source=researchgate.net&utm_medium=article
39. R. Fazaeli, M. Monajjemi, F. Ataherian & K. Zare. Solvent effects on relative stabilities and 14N NMR shielding of cytosine tautomers: Continuous set of gauge transformation calculation using polarizable continuum model. Journal of Molecular Structure: THEOCHEM, 581(1-3), 51–58 (2002). https://doi.org/10.1016/S0166-1280(01)00744-8
40. M. Monajjemi, M.S. Hosseini & F. Mollaamin. Theoretical study of boron nitride nanotubes with armchair forms. Fullerenes Nanotubes and Carbon Nanostructures, 21(5), 381–393 (2013). https://doi.org/10.1080/1536383x.2011.629752
41. M. Monajjemi, V.S. Lee, M. Khaleghian, B. Honarparvar & F. Mollaamin. Theoretical description of electromagnetic nonbonded interactions of radical, cationic, and anionic NH2BHNBHNH2 inside of the B18N18 nanoring. Journal of Physical Chemistry C, 114(36), 15315–15330 (2010). https://doi.org/ 10.1021/jp104274z
42. M. Monajjemi & J.E. Boggs. A new generation of BnNn rings as a supplement to boron nitride tubes and cages. Journal of Physical Chemistry A, 117(7), 1670–1684 (2013). https://doi.org/10.1021/ jp312073q
43. M. Monajjemi. Metal-doped graphene layers composed with boron nitride–graphene as an insulator: a nano-capacitor. Journal of Molecular Modeling, 20(11), 2507 (2014). https://doi.org/ 10.1007/s00894-014-2507-y
44. M. Monajjemi. Non bonded interaction between BnNn (stator) and BN(-,0,+)B (rotor) systems: A quantum rotation in IR region. Chemical Physics, 425, 29–45 (2013). https://doi.org/10.1016/ j.chemphys.2013.07.014
45. M. Monajjemi, R. Wayne, Jr. & J.E. Boggs. NMR contour maps as a new parameter of carboxyl's OH groups in amino acids recognition: A reason of tRNA-amino acid conjugation. Chemical Physics, 433, 1–11 (2014). https://doi.org/10.1016/j.chemphys.2014.01.017
46. M.W. Libbrecht & W.S. Noble. Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332 (2015). https://doi.org/10.1038/nrg3920
47. B. Alipanahi, A. Delong, M.T. Weirauch & B.J. Frey. Predicting the sequence specifcities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 33, 831–838 (2015). https://doi.org/10.1038/nbt.3300
48. J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589 (2021). https://doi.org/10.1038/ s41586-021-03819-2
49. M. Monajjemi, S. Afsharnezhad, M.R. Jaafari, S. Mirdamadi, F.H. Mollaamin & H. Monajemi. Investigation of energy and NMR isotropic shift on the internal rotation Barrier of Θ4 dihedral angle of the DLPC: A GIAO study. Bulgarian Chemical Communications, 17(1), 55–69 (2008).
50. F. Mollaamin & M. Monajjemi. Electric and magnetic evaluation of aluminum–magnesium nanoalloy decorated with germanium through heterocyclic carbenes adsorption: A density functional theory study. Russian Journal of Physical Chemistry B, 17, 658–672 (2023). https://doi.org/ 10.1134/s1990793123030223
51. F. Mollaamin & M. Monajjemi. Adsorption ability of Ga5N10 nanomaterial for removing metal ions contamination from drinking water by DFT. International Journal of Quantum Chemistry, 124(2), e27348 (2024). https://doi.org/10.1002/qua.27348
52. F. Mollaamin & M. Monajjemi. Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method. Sensor Review, 43(4), 266–279 (2023). https://doi.org/10.1108/sr-03-2023-0040
53. F. Mollaamin & M. Monajjemi. Nanomaterials for sustainable energy in hydrogen-fuel cell: Functionalization and characterization of carbon nano-semiconductors with silicon, germanium, tin or lead through density functional theory study. Russian Journal of Physical Chemistry B, 18, 607–623 (2024). https://doi.org/10.1134/s1990793124020271
54. F. Mollaamin & M. Monajjemi. In situ Ti-embedded SiC as chemiresistive nanosensor for safety monitoring of CO, CO2, NO, NO2: Molecular modelling by conceptual density functional theory. Russian Journal of Physical Chemistry B, 18, 49–66 (2024). https://doi.org/10.1134/s1990793124010159
55. F. Mollaamin, S. Shahriari & M. Monajjemi. Influence of transition metals for emergence of energy storage in fuel cells through hydrogen adsorption on the MgAl surface. Russian Journal of Physical Chemistry B, 18, 398–418 (2024). https://doi.org/10.1134/s199079312402026x
56. M. Popova, O. Isayev & A. Tropsha. Deep reinforcement learning for de novo drug design. Science Advances, 4(7), eaap7885 (2018). https://doi.org/10.1126/sciadv.aap7885
57. Z. Li, E. Gao, J. Zhou, W. Han, X. Xu & X. Gao. Applications of deep learning in understanding gene regulation. Cell Reports Methods, 3(1), 100384 (2023). https://doi.org/10.1016/j.crmeth.2022. 100384
58. Z. Hou, Y. Yang, Z. Ma, K.-C. Wong & X. Li. Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning. Communications Biology, 6, 73 (2023). https://doi.org/10.1038/s42003-023-04462-5
59. P. Lin, Y. Yan & S.-Y. Huang. DeepHomo2.0: Improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning. Briefings in Bioinformatics, 24(1), bbac499 (2023). https://doi.org/10.1093/bib/bbac499
60. M. Torrisi, G. Pollastri & Q. Le. Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal, 18, 1301–1310 (2020). https://doi.org/10.1016/ j.csbj.2019.12.011
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Cómo citar
Licencia
Derechos de autor 2026 Revista Colombiana de Ciencias Químico-Farmacéuticas
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
El Departamento de Farmacia de la Facultad de Ciencias de la Universidad Nacional de Colombia autoriza la fotocopia de artículos y textos para fines de uso académico o interno de las instituciones citando la fuente. Las ideas emitidas por los autores son responsabilidad expresa de estos y no de la revista.
Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons de Atribución 4.0 aprobada en Colombia. Consulte la normativa en: http://co.creativecommons.org/?page_id=13
