Open Access Open Access  Restricted Access Subscription or Fee Access

Structure based pharmacophore Modelling of compounds against Neurodegenerative disorder – Amyotrophic Lateral sclerosis

Mansi Agrawal


Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the progressive degeneration of motor neurons, leading to muscle dysfunction, paralysis, and eventually death. These diseases are classified as ALS variations because autopsies reveal abnormalities in both upper and lower motor neurons. These disorders account for only 10% of all adult-onset motor neuron disease cases. Despite extensive research efforts, effective therapeutic interventions for ALS remain limited. Structure-based pharmacophore modeling have emerged as valuable computational approaches in drug discovery, offering insights into the identification and optimization of potential ALS treatments. The importance of target selection, receptor preparation, and the integration of experimental data in constructing pharmacophore models for ALS-related proteins is the core.

Pharmacophore modelling is fundamentally based on the idea that the spatial arrangement of a molecule's functional groups and how they interact with the target define a molecule's biological activity. The key characteristics and spatial configuration necessary for a molecule to bind to and interact with the target are represented hypothetically in three dimensions (3D) by a pharmacophore. The compounds were selected from different literatures on the basis of their activity in IC50 on the SH-SY5Y cell lines acting against amyotrophic lateral sclerosis. The structures were optimised with respect to MMFF and then used for forming a pharmacophore hypothesis to define the active sites taking part in the bonding process to form the ligand-protein complex and provide a therapeutic effect. The hypothesis was validated with the active and decoy set to check which compound superimposes the hypothesis in the maximum degree. Maximum score of 0.71 was procured. And this hypothesis was superlative one .The process of pharmacophore modelling can be very useful for analysing and studying the further more compound that may show the maximum compatibility and binding with the target to produce more effect against the disease.


Pharmacophore modeling, neurodegenerative disease, Amyotrophic lateral sclerosis, Motor neuron disease, Drug discovery

Full Text:



Archer MC, Hall PH, Morgan JC. Accuracy of clinical diagnosis of Alzheimer's disease in Alzheimer's Disease Centers (ADCS). Alzheimer's & Dementia. 2017;13(7):P800– P801.

Al-Chalabi A, Andersen PM, Chioza B. Recessive amyotrophic lateral sclerosis families with the D90A SOD1 mutation share a common founder: evidence for a linked protective factor. Hum Mol Genet. 1998;7:2045–2050.

Bredesen DE, Rao RV, Mehlen P. Cell death in the nervous system. Nature. 2006;443(7113):796–802.

Cabral-Costa JV, Kowaltowski AJ. Neurological disorders and mitochondria. Mol Aspects Med. 2020;71:100826.

Dunnen JT, Dalgleish R, Maglott DR, Hart RK, Greenblatt MS, McGowan-Jordan J. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat. 2016;37:564–569.

Dugan TR, Kuntz ID. Pharmacophore models and their applications in drug discovery: Challenges and recent advances. ACS Chem Biol. 2020;15(4):813–825.

Fathima F, Shenoy G. Design of potent HDAC inhibitors: Pharmacophore, fingerprint- based 2D-QSAR, atom-based 3D-QSAR, and molecular docking studies. In: Proceedings of International Conference on Drug Discovery (ICDD); 2020.

Ghose AK, Wendoloski JJ. Pharmacophore modelling: methods, experimental verification and applications. In: 3D QSAR in drug design: ligand-protein interactions and molecular similarity. 2002. p. 253–271.

Hideshima M, Beck G, Yamadera M, Motoyama Y, Ikenaka K, Kakuda K, et al. A clinicopathological study of ALS with L126S mutation in the SOD1 gene presenting with isolated inferior olivary hypertrophy. Neuropathology. 2020;40:191–195.

Kohlbacher SM, Schmid M, Seidel T, Langer T. Applications of the novel quantitative pharmacophore activity relationship method QPhAR in virtual screening and lead- optimisation. Pharmaceuticals. 2022;15(9):1122.

Kovalevich J, Santerre M, Langford D. Considerations for the use of SH-SY5Y neuroblastoma cells in neurobiology. Methods Mol Biol. 2021;2311:9–23.

Langer T, Hoffmann RD. Pharmacophore modelling: applications in drug discovery. Expert Opin Drug Discov. 2006;1(3):261–267.

Mitsumoto H, et al. Oxidative stress biomarkers in sporadic ALS. Amyotroph Lateral Scler. 2008;9:177–183.

Mousa LA, Hatmal MMM, Taha M. Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as a case study. J Comput Aided Mol Des. 2022;36(1):39–62.

Nowak RJ, Cuny GD, Choi S, Lansbury PT, Ray SS. Improving binding specificity of pharmacological chaperones that target mutant superoxide dismutase-1 linked to familial amyotrophic lateral sclerosis using computational methods. J Med Chem. 2010;53(7):2709–2718.

Parekh N, Lakhani S, Patel A, Oza D, Patel B, Yadav R, Chaube U. Discovery of novel CaMK-II inhibitor for the possible mitigation of arrhythmia through pharmacophore modelling, virtual screening, molecular docking, and toxicity prediction. Artificial Intelligence Chemistry. 2023;100009.

Pereira TMC, Côco LZ, Ton AMM, Meyrelles SS, Campos-Toimil M, Campagnaro BP, Vasquez EC. The emerging scenario of the gut-brain axis: the therapeutic actions of the new actor Kefir against neurodegenerative diseases. Antioxidants. 2021;10(11):1845.

Priller C, Bauer T, Mitteregger G, Krebs B, Kretzschmar HA, Herms J. Synapse formation and function are modulated by the amyloid precursor protein. J Neurosci. 2006;26(27):7212–7221.

Rao SK, Jain R. Pharmacophore modeling in drug discovery and development: An overview. Med Chem Res. 2019;28(5):569–588.

Rencilin CF, Rosy JC, Sundar K. Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics. Int J Comput Biol Drug Des. 2023;15(4):316–335.

Rowland LP. Diagnosis of amyotrophic lateral sclerosis. J Neurol Sci. 1998;160:S6– S24.

Rubinstein DC. The roles of intracellular protein-degradation pathways in neurodegeneration. Nature. 2006;443(7113):7806.

Schreyer AM, Blundell TL. Pharmacophore modeling in drug discovery and its application to fragment-based design. Annu Rev Pharmacol Toxicol. 2022;62:295–314.

Srinivasan A, Page D, Camacho R, King R. Quantitative pharmacophore models with inductive logic programming. Machine Learning. 2006;64:65–90.

Strother L, Miles GB, Holiday AR, Cheng Y, Doherty GH. Long-term culture of SH- SY5Y neuroblastoma cells in the absence of neurotrophins: A novel model of neuronal ageing. J Neurosci Methods. 2021;362:109301.

Suzuki N, Maroof AM, Merkle FT, Koszka K, Intoh A, Armstrong I, et al. The mouse C9ORF72 ortholog is enriched in neurons known to degenerate in ALS and FTD. Nat Neurosci. 2013;16:1725–1727.

Szwabowski GL, Cole JA, Baker DL, Parrill AL. Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation. J Mol Graph Model. 2023;121:108429.

Verma RK, Dixit S. Pharmacophore-based virtual screening: An efficient approach for hit identification in drug discovery. Med Chem Res. 2023;32(1):1–25.

Yang J, Qiu M, Lu T, Yang S, Yu J, Lin J, et al. Discovery and verification of bitter components in Panax notoginseng based on the integrated strategy of pharmacophore model, system separation and bitter tracing technology. Food Chemistry. 2023;136716.

Yele V, Azam MA, Jupudi S. Ligand-based pharmacophore modelling, in silico virtual screening, molecular docking and molecular dynamic simulation study to identify novel Francisella tularensis ParE inhibitors. Chem Papers. 2020;74:4567–4580.

Zhang X, Sedykh A. Quantitative structure-pharmacokinetic relationships (QSPkR) and pharmacophore-based machine learning models for the prediction of volume of distribution. J Chem Inf Model. 2019;59(3):1253–1263.



  • There are currently no refbacks.