Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Shiammala, Periyasamy Natarajan; Duraimutharasan, Navaneetha Krishna Bose; Vaseeharan, Baskaralingam; Alothaim, Abdulaziz S.; Al-Malki, Esam S.; Snekaa, Babu; Safi, Sher Zaman; Singh, Sanjeev Kumar; Velmurugan, Devadasan; Selvaraj, Chandrabose

doi:10.1016/j.ymeth.2023.09.010

Cited by 14 publications

(2 citation statements)

References 68 publications

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“…Computer-aided drug screening can significantly enhance the process of identifying potential drug leads [81]. Discovering potent inhibitors with a restricted number of studies would pose a challenge [82].…”

Section: Discussionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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“…The traditional drug discovery process is time-consuming and expensive [4], and drug repositioning offers a cost-effective alternative to traditional methods by utilizing medications that have already been approved for other uses [5]. There are several successful examples of drug repositioning in the eld of pain management, such as duloxetine (a serotonin reuptake inhibitor), amitriptyline (a tricyclic antidepressant), and various antiepileptics (such as lamotrigine, gabapentin, and pregabalin), which underscores the advantage of drug repositioning for the development of new analgesics [6].…”

Section: Introductionmentioning

confidence: 99%

Nicorandil repurposing in orofacial pain: preclinical findings in adult zebrafish

de Araújo,

Leite,

Vieira-Neto

et al. 2024

Preprint

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This study investigated the orofacial antinociceptive activity of nicorandil in adult zebrafish and explored the involvement of TRP channels in this effect. Nicorandil, a known antianginal drug, reduced nociceptive behaviors induced by capsaicin (TRPV1 agonist), cinnamaldehyde (TRPA1 agonist), and menthol (TRPM8 agonist) without altering the locomotor activity of the zebrafish. Pre-treatment with specific TRPA1 and TRPV1 antagonists prevented the antinociceptive effects of nicorandil, indicating its action on these channels. Molecular docking studies supported these findings, demonstrating high chemical affinity and specific binding of nicorandil to the TRPV1 and TRPA1 channels, leading to stabilization and reduced biological activity of these channels. In contrast, the antinociceptive effect of nicorandil on menthol-induced nociception was not affected by a TRPM8 antagonist, suggesting that TRPM8 modulation is not involved in nicorandil’s mechanism of action. The study highlights the potential of nicorandil as an analgesic through its interaction with TRPV1 and TRPA1 channels, providing a molecular basis for repositioning nicorandil as an effective analgesic drug.

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Natural products as drug leads: exploring their potential in drug discovery and development

Singh,

Gupta,

Chanchal

et al. 2024

Naunyn-Schmiedeberg's Arch Pharmacol

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Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Cited by 14 publications

References 68 publications

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Nicorandil repurposing in orofacial pain: preclinical findings in adult zebrafish

Natural products as drug leads: exploring their potential in drug discovery and development

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