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

Acta Pharmaceutica Sinica B, Journal Year: 2022, Volume and Issue: 12(11), P. 4011 - 4039

Published: Aug. 27, 2022

Language: Английский

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Machine learning applications in drug development

Computational and Structural Biotechnology Journal, Journal Year: 2019, Volume and Issue: 18, P. 241 - 252

Published: Dec. 26, 2019

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, design largely automated drug development pipelines can now be envisioned. These may guide, or speed up, discovery; provide a better understanding diseases associated phenomena; help planning preclinical wet-lab experiments, even future clinical trials. This automation process might key current issue low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes methods: sequential recommender systems, which are active biomedical fields research.

Language: Английский

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Blockchain and artificial intelligence technology in e-Health

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 28(38), P. 52810 - 52831

Published: Sept. 2, 2021

Language: Английский

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Exploring different approaches to improve the success of drug discovery and development projects: a review

Future Journal of Pharmaceutical Sciences, Journal Year: 2020, Volume and Issue: 6(1)

Published: June 23, 2020

Abstract Background There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over last three decades. Despite increased investments research infrastructure by pharmaceutical companies technological advances scientific tools available, efforts to number molecules coming through drug development pipeline have largely unfruitful. Main body A non-systematic review current literature was undertaken enumerate various strategies employed improve success rates development. The covers exploitation genomics proteomics, complementarity target-based phenotypic efficacy screening platforms, repurposing repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, modeling artificial intelligence. Examples successful discoveries achieved application these are highlighted discussed herein. Conclusions Genomics proteomics uncovered wide array potential targets facilitative enhanced scrupulous target identification validation thus reducing efficacy-related attrition. When used complementarily, platforms would likely allow serendipitous discovery while increasing rationality design. Drug repositioning reduces financial risks accompanied time savings, prolonging patent exclusivity hence returns investment innovator company. Equally important, is cross-fertilization refinement ideas, sharing resources expertise, overhead costs early stages discovery. Underserved fields niche areas that may be experiment launch novel targets, exploiting incentivized benefits afforded regulatory authorities. Outsourcing allows pharma industries focus their core competencies deriving greater efficiency specialist contract organizations. existing emerging intelligence softwares silico computation enabling more efficient computer-aided Careful selection strategies, singly or combination, potentially harness innovation.

Language: Английский

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High-Throughput Screening Platforms in the Discovery of Novel Drugs for Neurodegenerative Diseases

Bioengineering, Journal Year: 2021, Volume and Issue: 8(2), P. 30 - 30

Published: Feb. 23, 2021

Neurodegenerative diseases (NDDs) are incurable and debilitating conditions that result in progressive degeneration and/or death of nerve cells the central nervous system (CNS). Identification viable therapeutic targets new treatments for CNS disorders particular, NDDs is a major challenge field drug discovery. These difficulties can be attributed to diversity involved, extreme complexity neural circuits, limited capacity tissue regeneration, our incomplete understanding underlying pathological processes. Drug discovery complex multidisciplinary process. The screening attrition rate current protocols mean only one may arise from millions screened compounds resulting need improve technologies address multiple causes attrition. This has identified screen larger libraries where use efficient high-throughput (HTS) becomes key HTS investigate hundreds thousands per day. However, if fewer could without compromising probability success, cost time would largely reduced. To end, recent advances computer-aided design, silico libraries, molecular docking software combined with upscaling cell-based platforms have evolved efficiency higher predictability clinical applicability. We review, here, increasing role contemporary processes, particular NDDs, evaluate criteria its successful application. also discuss requirement novel NDD therapies examine challenges validating developing NDDs.

Language: Английский

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Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(3), P. 2026 - 2026

Published: Jan. 19, 2023

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability happiness. But advancing a fresh medication is quite convoluted, costly, protracted operation, normally costing USD ~2.6 billion consuming mean time span 12 years. Methods cut back expenditure hasten new drug have prompted an arduous compelling brainstorming exercise in pharmaceutical industry. engagement Artificial Intelligence (AI), including deep-learning (DL) component particular, has been facilitated by employment classified big data, concert with strikingly reinforced computing prowess cloud storage, across all fields. AI energized computer-facilitated discovery. An unrestricted espousing machine learning (ML), especially DL, many scientific specialties, technological refinements hardware software, various aspects problem, sustain this progress. ML algorithms extensively engaged for DL methods, such artificial neural networks (ANNs) comprising multiple buried processing layers, late seen resurgence due their capability power automatic attribute elicitations from input coupled ability obtain nonlinear input-output pertinencies. Such features methods augment classical techniques which bank on human-contrived molecular descriptors. A major part early reluctance concerning utility begun melt, thereby medicinal chemistry. AI, along modern experimental technical knowledge, anticipated invigorate quest improved pharmaceuticals expeditious, economical, increasingly manner. DL-facilitated just initiated kickstarting some integral issues Many advances, “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, other ingenious exemplars, will definitely come pervasively widespread help dissect biggest, most intriguing inquiries. Open data allocation model augmentation exert decisive hold during progress employing AI. This review address impending utilizations refine bolster operation.

Language: Английский

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Graph neural networks for automated de novo drug design

Drug Discovery Today, Journal Year: 2021, Volume and Issue: 26(6), P. 1382 - 1393

Published: Feb. 18, 2021

Language: Английский

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Advanced machine-learning techniques in drug discovery

Drug Discovery Today, Journal Year: 2020, Volume and Issue: 26(3), P. 769 - 777

Published: Dec. 5, 2020

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do limitations become apparent. Such include need for big data, sparsity in and lack interpretability. It has also apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail advanced circumvent these challenges, with examples drawn from allied disciplines. addition, present emerging potential role discovery. presented herein anticipated expand applicability ML

Language: Английский

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Intelligent Computing: The Latest Advances, Challenges, and Future

Intelligent Computing, Journal Year: 2023, Volume and Issue: 2

Published: Jan. 1, 2023

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed emergence intelligent computing, new computing paradigm that reshaping traditional and promoting digital revolution era big data, artificial intelligence, internet things with theories, architectures, methods, systems, applications. Intelligent has greatly broadened scope extending it from on data to increasingly diverse paradigms such as perceptual cognitive autonomous human–computer fusion intelligence. Intelligence undergone paths different evolution for long time but become intertwined years: not only intelligence oriented also driven. Such cross-fertilization prompted rapid advancement computing. still its infancy, an abundance innovations applications expected occur soon. We present first comprehensive survey literature covering theory fundamentals, technological important applications, challenges, future perspectives. believe this highly timely will provide reference cast valuable insights into academic industrial researchers practitioners.

Language: Английский

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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review

Analytica Chimica Acta, Journal Year: 2021, Volume and Issue: 1161, P. 338403 - 338403

Published: March 15, 2021

Language: Английский

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