Author(s): Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das

Email(s): prachisandeepk@gmail.com

DOI: 10.52711/0974-360X.2024.00218   

Address: Prachi Parvatikar1*, Joy Hoskeri3, Bhagirathi Hallali4, Kusal K Das2
1Dept of Biotechnology, Allied Health Sciences, BLDE(DU), Vijayapura 586103, India.
2Laboratory of Vascular Physiology and Medicine, Dept of Physiology, Shri B.M. Patil Medical College, Hospital and Research centre.
3Dept of Bioinformatics, Karnataka State Akkamahadevi Women’s University, Vijyaypura, India.
4Dept of Computer Science, Govt Degree College, Raibag, Belgavi, India.
*Corresponding Author

Published In:   Volume - 17,      Issue - 3,     Year - 2024


ABSTRACT:
Proteochemometric (PCM) modelling is the new way of developing quantitative structure activity relationship models. It is computational method in which multiple ligands and multiple targets are used to predict bioactivity. PCM is based on three important components; the descriptors, bioactivity data and connecting link between descriptors and bioactivity data. In recent years PCM modelling has become more popular in drug discovery area as it has advantage of application of different descriptors, bioactivity data and machine learning algorithms. The performance of PCM is enhanced to traditional interaction pattern by application of different descriptors such as target descriptors and cross-term descriptors. So, in current review PCM and different descriptors used in PCM development and its application in various field of drug discovery has been discussed.


Cite this article:
Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das. Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing. Research Journal of Pharmacy and Technology. 2024; 17(3):1382-5. doi: 10.52711/0974-360X.2024.00218

Cite(Electronic):
Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das. Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing. Research Journal of Pharmacy and Technology. 2024; 17(3):1382-5. doi: 10.52711/0974-360X.2024.00218   Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2024-17-3-69


REFERENCES:
1.    Rohrer, S. P., Birzin, E. T., Mosley, R. T., et al. Rapid identification of subtype-selective agonists of the somatostatin receptor through combinatorial chemistry. Sci 1998; 282(5389). doi.org/10.1126/science.282.5389.737
2.    Hansch, Corwin, and A. Ruth Steward. The use of substituent constants in the analysis of the structure-activity relationship in penicillin derivatives. JMedi Chem 1964: 691-694.0.1021/jm00336a001
3.    Hu, R., Doucet, J. P., Delamar, M., and Zhang, R. QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods. Eurj of medi chemi. 2009, 44(5).10.1016/j.ejmech.2008.10.021
4.    Lapinsh M, Prusis P, Gutcaits A, Lundstedt T, Wikberg JE. Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions. Bioch et Biophy Acta (BBA) 2001;180-90.10.1016/s0304-4165(00)00187-2
5.    Van Westen, G. J., Wegner, J. K., IJzerman, A. P., Van Vlijmen, H. W., and Bender, A. Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. MedChemComm, 2011, 2(1).doi.org/10.1039/C0MD00165A
6.    Cortes-Ciriano, I., and Bender, A. Reliable prediction errors for deep neural networks using test-time dropout. Jchem infor and mod.2019,59(7).doi.org/10.1021/acs.jcim.9b00297
7.    Jayatilleke, P. R., Nair, A. C., Zauhar, R., and Welsh, W. J. Computational Studies on HIV-1 Protease Inhibitors: Influence of Calculated Inhibitor− Enzyme Binding Affinities on the Statistical Quality of 3D-QSAR CoMFA Models. Jmed chem.2000,43(23).0.1021/jm9905357
8.    Junaid, M., Lapins, M., Eklund, M., Spjuth, O., and Wikberg, J. E. Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors. PloS one, 2010,5(12).doi.org/10.1371/journal.pone.0014353
9.    Burggraaff, L., Lenselink, E. B., Jespers, et al. Successive statistical and structure-based modeling to identify chemically novel kinase inhibitors. J. Chem. Inf. Model., 2020,60(9)10.1021/acs.jcim.9b01204
10.    Bongers BJ, IJzerman AP, Van Westen GJ. Proteochemometrics–recent developments in bioactivity and selectivity modeling. Drug Discov. Today Technol. 2019,32:89-98.10.1016/j.ddtec.2020.08.003
11.    Baumann, D., and Baumann, K. (2014). Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation. J Cheminform, 6(1), 1-19.https://doi.org/10.1186/s13321-014-0047-1
12.    Giblin, K. A., Hughes, S. J., Boyd, H., Hansson, P., and Bender, A. Prospectively validated proteochemometric models for the prediction of small-molecule binding to bromodomain proteins. J Chem Inf Model, 2018,58(9),0.1021/acs.jcim.8b00400.
13.    Cortes-Ciriano, I., Murrell, D. S., van Westen, G. J., Bender, A., and Malliavin, T. E. Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling. J Cheminform, 2015,7(1).10.1186/s13321-014-0049-z
14.    Manoharan, P., Chennoju, K., and Ghoshal, N. Target specific proteochemometric model development for BACE1–Protein flexibility and structural water are critical in virtual screening. Molecular BioSystems, 2015, 11(7),10.1039/C5MB00088B
15.    Kramer, C., and Gedeck, P. Global free energy scoring functions based on distance-dependent atom-type pair descriptors. J Chem Inf Model, 2011,51(3), 707-720.
16.    Karasev, D., Sobolev, B., Lagunin, A., et al. Prediction of protein–ligand interaction based on the positional similarity scores derived from amino acid sequences. Int. J. Mol. Sci. 2020, 21(1), 24; https://doi.org/10.3390/ijms21010024
17.    Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., and Kanehisa, M. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13), 10.1093/bioinformatics/btn162
18.    Subramanian, V., Ain, Q. U., Henno, H., et al. 3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases. MedChemComm, 2017,8(5), 10.1039/C6MD00701E
19.    Ain, Q. U., Méndez-Lucio, O., Ciriano, I. C. Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features. Integr Biol (Camb), 2014,6(11),10.1039/c4ib00175c
20.    Prado-Prado, F., García-Mera, X., Abeijón, P., Alonso, N., Caamaño, O., Yáñez, M., ... and González-Díaz, H. (2011). Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepaticaEur J Med Chem, 46(4), 10.1016/j.ejmech.2011.01.023
21.    González-Díaz, H., Riera-Fernández, P., Pazos, A., and Munteanu, C. R. . The Rücker–Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics. Biosystems, 2013,111(3).10.1016/j.biosystems.2013.02.006
22.    Riera-Fernandez, P., R Munteanu, C., Dorado, J., et al. From chemical graphs in computer-aided drug design to general Markov-Galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks. Curr Comput Aided Drug Des, 2011,7(4), 315-337.10.2174/157340911798260340
23.    Hariri, S., Rasti, B., Mirpour, M., et al. Structural insights into the origin of phosphoinositide 3-kinase inhibition. Structural Chemistry, 2020,31(4), 1505-1522.10.1007/s11224-020-01510-2
24.    Bender, A., and Glen, R. C. Molecular similarity: a key technique in molecular informatics. Organic and biomolecular chemistry, 2004,2(22),10.1039/B409813G
25.    Nabu, S., Nantasenamat, C., Owasirikul, et al. Proteochemometric model for predicting the inhibition of penicillin-binding proteins. Journal of computer-aided molecular design, 2015,29(2), 127-141.10.1007/s10822-014-9809-0
26.    Consonni, V., Ballabio, D., and Todeschini, R. Comments on the definition of the Q 2 parameter for QSAR validation. J Chem Inf Model, 2009, 49(7), 10.1021/ci900115y.
27.    Tian, F., Zhou, P., and Li, Z. T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides. J. Mol. Struct, 2007,830(1-3).10.1016/j.molstruc.2006.07.004
28.    Bosc, N., Wroblowski, B., Meyer, C., and Bonnet, P. Prediction of protein kinase–ligand interactions through 2.5 D kinochemometrics. J Chem Inf Model, 2017,57(1)10.1021/acs.jcim.6b00520.
29.    Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., et al. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. J Chem Inf Model, 2015,7(1),10.1186/s13321-015-0063-9
30.    Schaduangrat, N., Anuwongcharoen, N., Phanus-umporn, C., Proteochemometric Modeling for Drug Repositioning. In In Silico drug design. 2019, 281-302, Academic Press.10.1016/B978-0-12-816125-8.00010-9

Recomonded Articles:

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

1.3
2021CiteScore
 
56th percentile
Powered by  Scopus


SCImago Journal & Country Rank


Recent Articles




Tags


Not Available