Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

Saifi, Ifra; Bhat, Basharat Ahmad; Hamdani, Syed Suhail; Bhat, Umar Yousuf; Lobato-Tapia, Carlos Alberto; Mir, Mushtaq Ahmad; Dar, Tanvir Ul Hasan; Ganie, Showkat Ahmad

doi:10.1080/07391102.2023.2234039

Cited by 7 publications

(5 citation statements)

References 150 publications

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“…By developing ML models, new systems can be created with the ability to learn and improve without undergoing any previous programming. − The accuracy and effectiveness of these models must meet criteria of responsibility, such as systematic identification and search for regularities to guarantee the prediction and ensure that the analysis was carried out correctly …”

Section: Introductionmentioning

confidence: 99%

“…27−29 The accuracy and effectiveness of these models must meet criteria of responsibility, such as systematic identification and search for regularities to guarantee the prediction and ensure that the analysis was carried out correctly. 30 These kinds of methods have been used in other scientific fields such as robotics, data mining, chemistry, and biotechnology, etc. 31−33 However, one of the limitations of these conventional methods arises from the fact that they don't cover large data sets that appear in trials.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

Velásquez-López,

Ruiz-Escudero,

Arrasate

et al. 2024

J. Chem. Inf. Model.

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The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

Velásquez-López,

Ruiz-Escudero,

Arrasate

et al. 2024

J. Chem. Inf. Model.

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“…Nonetheless, significant challenges persist, including the requirement for high-quality data, accurate ADMET prediction, and prevention of model overfitting. This review explores the significance of lead optimization across chemical and pharmaceutical dimensions while delving into AI methods' role in advancing this critical stage of drug development [16,23].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Niazi,

Mariam,

Magoola

2024

Preprint

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Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

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“…The versatile approach of molecular docking, which is based on the theory of ligand-receptor interactions, is widely used in drug discovery to understand how compounds bind with their molecular targets ( Tiwari and Singh, 2022 ; Saifi et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Network pharmacology combined with molecular docking and in vitro verification reveals the therapeutic potential of Delphinium roylei munz constituents on breast carcinoma

Mir,

Bhat,

Kumar

et al. 2023

Front. Pharmacol.

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Delphinium roylei Munz is an indigenous medicinal plant to India where its activity against cancer has not been previously investigated, and its specific interactions of bioactive compounds with vulnerable breast cancer drug targets remain largely unknown. Therefore, in the current study, we aimed to evaluate the anti-breast cancer activity of different extracts of D. roylei against breast cancer and deciphering the molecular mechanism by Network Pharmacology combined with Molecular Docking and in vitro verification. The experimental plant was extracted with various organic solvents according to their polarity index. Phytocompounds were identified by High resolution-liquid chromatography-mass spectrometry (HR-LC/MS) technique, and SwissADME programme evaluated their physicochemical properties. Next, target(s) associated with the obtained bioactives or breast cancer-related targets were retrieved by public databases, and the Venn diagram selected the overlapping targets. The networks between overlapping targets and bioactive were visualized, constructed, and analyzed by STRING programme and Cytoscape software. Finally, we implemented a molecular docking test (MDT) using AutoDock Vina to explore key target(s) and compound(s). HR-LC/MS detected hundreds of phytocompounds, and few were accepted by Lipinski’s rules after virtual screening and therefore classified as drug-like compounds (DLCs). A total of 464 potential target genes were attained for the nine quantitative phytocompounds and using Gene Cards, OMIM and DisGeNET platforms, 12063 disease targets linked to breast cancer were retrieved. With Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment, a total of 20 signalling pathways were manifested, and a hub signalling pathway (PI3K-Akt signalling pathway), a key target (Akt1), and a key compound (8-Hydroxycoumarin) were selected among the 20 signalling pathways via molecular docking studies. The molecular docking investigation revealed that among the nine phytoconstituents, 8-hydroxycoumarin showed the best binding energy (−9.2 kcal/mol) with the Akt1 breast cancer target. 8-hydroxycoumarin followed all the ADME property prediction using SwissADME, and 100 nanoseconds (ns) MD simulations of 8-hydroxycoumarin complexes with Akt1 were found to be stable. Furthermore, D. roylei extracts also showed significant antioxidant and anticancer activity through in vitro studies. Our findings indicated for the first time that D. roylei extracts could be used in the treatment of BC.

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Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

Cited by 7 publications

References 150 publications

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Network pharmacology combined with molecular docking and in vitro verification reveals the therapeutic potential of Delphinium roylei munz constituents on breast carcinoma

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