Comparative analysis of an anthraquinone and chalcone derivatives-based virtual combinatorial library. A cheminformatics “proof-of-concept” study

Moshawih, Said; Hadikhani, Parham; Fatima, Ambrin; Goh, Hui Poh; Kifli, Nurolaini; Kotra, Vijay; Goh, Khang Wen; Ming, Long Chiau

doi:10.1016/j.jmgm.2022.108307

Cited by 6 publications

(6 citation statements)

References 84 publications

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“…Crystal structures of macromolecular targets (AA2AR, AKT1, CDK2, DPP4, PPARG, and EGFR) were obtained from DUD-E, along with their corresponding sets of active and decoy ligands. Active compounds for TDP1 and the p53 suppressor protein were sourced from PubChem and the scientific literature, encompassing anthraquinones and chalcone chemical classes [ 33 – 35 ].…”

Section: Methodsmentioning

confidence: 99%

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

Moshawih,

Bu,

Goh

et al. 2024

J Cheminform

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In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

Moshawih,

Bu,

Goh

et al. 2024

J Cheminform

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“…In the expansive realm of chemical space, the pharmaceutical industry faces the continual challenge of identifying new active pharmaceutical ingredients (APIs) for diverse diseases [ 159 ]. High throughput screening (HTS), despite its advancements in recent decades, remains resource-intensive and often yields unsuitable hits for drug development.…”

Section: Future Trends For the Digital Transformation Of In The Pharm...mentioning

confidence: 99%

The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift

Almeman

2024

J Health Popul Nutr

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In the face of rapid technological advancement, the pharmacy sector is undergoing a significant digital transformation. This review explores the transformative impact of digitalization in the global pharmacy sector. We illustrated how advancements in technologies like artificial intelligence, blockchain, and online platforms are reshaping pharmacy services and education. The paper provides a comprehensive overview of the growth of online pharmacy platforms and the pivotal role of telepharmacy and telehealth during the COVID-19 pandemic. Additionally, it discusses the burgeoning cosmeceutical market within online pharmacies, the regulatory challenges faced globally, and the private sector’s influence on healthcare technology. Conclusively, the paper highlights future trends and technological innovations, underscoring the dynamic evolution of the pharmacy landscape in response to digital transformation.

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“…Combinatorial library has been used in natural product research to create databases of natural product analogues with drug-like properties [ 23 ]. This strategy can uncover the potential of natural products with privileged scaffold for new drug design and discovery, for instance, the anthraquinone- and chalcone- derivatives that showed a wide spectrum of biological effects on many different macromolecular targets responsible for human diseases including cancer were used to construct virtual library as the starting point of new drug research [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

Chua,

Moshawih,

Kifli

et al. 2024

PLoS ONE

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Background In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment. Methods The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the “International prospective register of systematic reviews” database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 “CADD or virtual screening”, concept 2 “anthraquinone” and concept 3 “cancer”. The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023. Results Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion. Conclusion By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

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Comparative analysis of an anthraquinone and chalcone derivatives-based virtual combinatorial library. A cheminformatics “proof-of-concept” study

Cited by 6 publications

References 84 publications

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

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