QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5’ Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum

Asmare, Misgana Mengistu; Nitin, Nitin; Yun, Soon‐Il; Mahapatra, Rajani Kanta

doi:10.1016/j.jmgm.2021.108108

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Another putative drug target could be the oxidoreductase inosine 5′-monophosphate dehydrogenase (IMPDH) essential for guanine synthesis in Cryptosporidium . Various inhibitors for CpIMPDH have been reported (Kirubakaran et al 2012 ; Gorla et al 2014 ; Sun et al 2014 ; Shigetomi et al 2019 ; Asmare et al 2022 ). Similarly, another study reported the use of Phylomer® designed peptides as an inhibitor of CpIMPDH, effectively reducing Cryptosporidium growth (Jefferies et al 2015 ).…”

Section: Novel Drug Targetsmentioning

confidence: 99%

An update on Cryptosporidium biology and therapeutic avenues

et al. 2022

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Cryptosporidium species has been identified as an important pediatric diarrheal pathogen in resource-limited countries, particularly in very young children (0–24 months). However, the only available drug (nitazoxanide) has limited efficacy and can only be prescribed in a medical setting to children older than one year. Many drug development projects have started to investigate new therapeutic avenues. Cryptosporidium ’s unique biology is challenging for the traditional drug discovery pipeline and requires novel drug screening approaches. Notably, in recent years, new methods of oocyst generation, in vitro processing, and continuous three-dimensional cultivation capacities have been developed. This has enabled more physiologically pertinent research assays for inhibitor discovery. In a short time, many great strides have been made in the development of anti- Cryptosporidium drugs. These are expected to eventually turn into clinical candidates for cryptosporidiosis treatment in the future. This review describes the latest development in Cryptosporidium biology, genomics, transcriptomics of the parasite, assay development, and new drug discovery.

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Section: Novel Drug Targetsmentioning

confidence: 99%

An update on Cryptosporidium biology and therapeutic avenues

et al. 2022

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“…Something to keep in mind is that conventional computational models and DL models are used together to maximise the potential of each [ 168 ]. VS is also impacted by DL.…”

Section: Ai and Ddmentioning

confidence: 99%

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

Crouzet,

Lopez,

Riss Yaw

et al. 2024

Molecules

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The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein–protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

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“…A particularly beneficial undertaking in the field of drug discovery and development is running virtual screening (VS) programs targeted at drug repurposing, which entails investigating additional or subsequent therapeutic uses for well-established medicines. The utilization of VS to analyze chemical libraries that compile well-known therapeutics can be viewed as a type of knowledge-based rational drug repositioning [12][13][14][15][16] (based on chemo-and bioinformatics, among other things), which has recently been recognized as a useful tactic to help find new treatments for uncommon and untreated conditions [17][18][19]. The resulting compounds of VS are 1012 and 340 from pharmacophore and deep learning, respectively.…”

Section: Introductionmentioning

confidence: 99%

Integrated Computational Approaches for Drug Design Targeting Cruzipain

Parvez,

Lee,

Alam

et al. 2024

IJMS

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Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

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QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5’ Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum

Cited by 6 publications

References 37 publications

An update on Cryptosporidium biology and therapeutic avenues

An update on Cryptosporidium biology and therapeutic avenues

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

Integrated Computational Approaches for Drug Design Targeting Cruzipain

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