Artificial intelligence in drug discovery and development

Drug Discovery Today, Год журнала: 2020, Номер 26(1), С. 80 - 93

Опубликована: Окт. 21, 2020

Язык: Английский

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Molecular Diversity, Год журнала: 2021, Номер 25(3), С. 1315 - 1360

Опубликована: Апрель 12, 2021

Drug designing and development is an important area of research for pharmaceutical companies chemical scientists. However, low efficacy, off-target delivery, time consumption, high cost impose a hurdle challenges that impact drug design discovery. Further, complex big data from genomics, proteomics, microarray data, clinical trials also obstacle in the discovery pipeline. Artificial intelligence machine learning technology play crucial role development. In other words, artificial neural networks deep algorithms have modernized area. Machine been implemented several processes such as peptide synthesis, structure-based virtual screening, ligand-based toxicity prediction, monitoring release, pharmacophore modeling, quantitative structure-activity relationship, repositioning, polypharmacology, physiochemical activity. Evidence past strengthens implementation this field. Moreover, novel mining, curation, management techniques provided critical support to recently developed modeling algorithms. summary, advancements provide excellent opportunity rational process, which will eventually mankind. The primary concern associated with consumption production cost. inefficiency, inaccurate target inappropriate dosage are hurdles inhibit process delivery With technology, computer-aided integrating can eliminate traditional referred superset comprising learning, whereas comprises supervised unsupervised reinforcement learning. subset has extensively network, vector machines, classification regression, generative adversarial networks, symbolic meta-learning examples applied process. different areas synthesis molecule design, screening molecular docking, relationship protein misfolding protein-protein interactions, pathway identification polypharmacology. principles active inactive, pre-clinical development, secondary biomarker manufacturing, bioactivity properties, prediction toxicity, mode action.

Язык: Английский

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DrugBank 6.0: the DrugBank Knowledgebase for 2024

Nucleic Acids Research, Год журнала: 2023, Номер 52(D1), С. D1265 - D1275

Опубликована: Ноя. 11, 2023

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. is widely used across many diverse biomedical research clinical applications, averages more than 30 million views/year. Since its last update 2018, we have been actively enhancing quantity quality of drug data this knowledgebase. In latest release (DrugBank 6.0), number FDA approved drugs from 2646 4563 (a 72% increase), investigational 3394 6231 38% drug-drug interactions increased 365 984 1 413 300% drug-food expanded 1195 2475 200% increase). addition notable expansion database size, added thousands new, colorful, richly annotated pathways depicting mechanisms metabolism. Likewise, existing datasets significantly improved expanded, by adding information on indications, interactions, other relevant types 11 891 drugs. We also experimental predicted MS/MS spectra, 1D/2D-NMR CCS (collision cross section), RT (retention time) RI index) 9464 DrugBank's 710 small molecule These improvements should make 6.0 even useful a much wider audience ranging medicinal chemists metabolomics specialists pharmacologists.

Язык: Английский

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Machine Learning Methods in Drug Discovery

Molecules, Год журнала: 2020, Номер 25(22), С. 5277 - 5277

Опубликована: Ноя. 12, 2020

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields industries. In the drug discovery development, machine learning been used development novel candidates. methods designing targets now routinely combine deep algorithms to enhance efficiency, efficacy, quality developed outputs. generation incorporation big data, through technologies such as high-throughput screening high through-put computational analysis databases both lead target discovery, has increased reliability incorporated techniques. use these virtual encompassing online also highlighted developing synthesis pathways. this review, utilized associated will be discussed. applications that produce promising results reviewed.

Язык: Английский

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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

Nature Biomedical Engineering, Год журнала: 2021, Номер 5(6), С. 613 - 623

Опубликована: Март 11, 2021

Язык: Английский

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Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector

Deleted Journal, Год журнала: 2023, Номер 1(2), С. 731 - 738

Опубликована: Фев. 8, 2023

Artificial intelligence (AI) has the potential to make substantial progress toward goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path ultimately become a mature effective tool for sector. Besides this AI-based systems raise concerns regarding data security privacy. Because health records are important vulnerable, hackers often target them during breaches. The absence standard guidelines moral use ML in only served worsen situation. There is debate about how far artificial may be utilized ethically settings since there no universal use. Therefore, maintaining confidentiality medical crucial. This study enlightens possible drawbacks implementation sector their solutions overcome these situations.

Язык: Английский

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Predicting drug–disease associations through layer attention graph convolutional network

Briefings in Bioinformatics, Год журнала: 2020, Номер 22(4)

Опубликована: Сен. 1, 2020

Abstract Background: Determining drug–disease associations is an integral part in the process of drug development. However, identification through wet experiments costly and inefficient. Hence, development efficient high-accuracy computational methods for predicting great significance. Results: In this paper, we propose a novel method named as layer attention graph convolutional network (LAGCN) association prediction. Specifically, LAGCN first integrates known associations, drug–drug similarities disease–disease into heterogeneous network, applies convolution operation to learn embeddings drugs diseases. Second, combines from multiple layers using mechanism. Third, unobserved are scored based on integrated embeddings. Evaluated by 5-fold cross-validations, achieves area under precision–recall curve 0.3168 receiver–operating characteristic 0.8750, which better than results existing state-of-the-art prediction baseline methods. The case study shows that can discover not curated our dataset. Conclusion: useful tool associations. This reveals different reflect proximities orders, combining mechanism improve performances.

Язык: Английский

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Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Medicinal Research Reviews, Год журнала: 2020, Номер 41(3), С. 1427 - 1473

Опубликована: Дек. 9, 2020

Abstract Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) remains the most challenging area drug discovery, accompanied with long timelines and high attrition rates. With rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) machine learning (ML) have emerged as an indispensable tool to draw meaningful insights improve decision making discovery. Thanks advancements AI ML algorithms, now AI/ML‐driven solutions unprecedented potential accelerate process CNS discovery better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical efforts their implementations area. After introducing AI/ML models well conceptualization preparation, outline applications technologies several key procedures including target identification, compound screening, hit/lead generation optimization, response synergy prediction, de novo design, repurposing. We review current state‐of‐the‐art AI/ML‐guided focusing on blood–brain barrier permeability prediction implementation into neurological diseases. Finally, discuss major challenges limitations approaches possible future directions that may provide resolutions these difficulties.

Язык: Английский

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New opportunities and challenges of natural products research: When target identification meets single-cell multiomics

Acta Pharmaceutica Sinica B, Год журнала: 2022, Номер 12(11), С. 4011 - 4039

Опубликована: Авг. 27, 2022

Язык: Английский

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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

International Journal of Molecular Sciences, Год журнала: 2021, Номер 22(4), С. 1676 - 1676

Опубликована: Фев. 7, 2021

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of active site biological target or its known binders, respectively. Artificial intelligence, including ma-chine learning, an emerging field has positively impacted discovery process. Deep reinforcement learning subdivision machine combines artificial neural networks reinforcement-learning architectures. This method successfully been em-ployed to develop de approaches using variety recurrent networks, convolutional generative adversarial autoencoders. review article summarizes advances in conventional growth algorithms advanced machine-learning methodologies high-lights hot topics for further development.

Язык: Английский

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