Artificial intelligence in drug discovery and development

Drug Discovery Today, Journal Year: 2020, Volume and Issue: 26(1), P. 80 - 93

Published: Oct. 21, 2020

Language: Английский

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

Molecular Diversity, Journal Year: 2021, Volume and Issue: 25(3), P. 1315 - 1360

Published: April 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.

Language: Английский

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Rethinking drug design in the artificial intelligence era

Nature Reviews Drug Discovery, Journal Year: 2019, Volume and Issue: 19(5), P. 353 - 364

Published: Dec. 4, 2019

Language: Английский

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The role of artificial intelligence in healthcare: a structured literature review

BMC Medical Informatics and Decision Making, Journal Year: 2021, Volume and Issue: 21(1)

Published: April 10, 2021

Abstract Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic a multi-disciplinary perspective, including accounting, business management, decision sciences professions. Methods The structured literature review with its reliable replicable research protocol allowed to extract 288 peer-reviewed papers Scopus. authors used qualitative quantitative variables analyse authors, journals, keywords, collaboration networks among researchers. Additionally, paper benefited Bibliometrix R software package. Results investigation showed that field emerging. It focuses on services predictive medicine, patient data diagnostics, clinical decision-making. United States, China, Kingdom contributed highest number of studies. Keyword analysis revealed AI can support physicians making diagnosis, predicting spread diseases customising treatment paths. Conclusions reveals several applications for stream has not fully been covered. For instance, projects require skills quality awareness data-intensive knowledge-based management. Insights help professionals understand address future field.

Language: Английский

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Computational approaches streamlining drug discovery

Nature, Journal Year: 2023, Volume and Issue: 616(7958), P. 673 - 685

Published: April 26, 2023

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This is largely defined by flood of data on ligand properties binding to therapeutic targets their 3D structures, abundant computing capacities advent on-demand virtual libraries drug-like small molecules billions. Taking full advantage these resources requires fast methods effective screening. includes structure-based screening gigascale chemical spaces, further facilitated iterative approaches. Highly synergistic are developments deep learning predictions target activities lieu receptor structure. Here we review recent advances technologies, potential reshaping whole process development, as well challenges they encounter. We also discuss how rapid identification highly diverse, potent, target-selective ligands protein can democratize process, presenting new opportunities cost-effective development safer more small-molecule treatments. Recent approaches application streamlining discussed.

Language: Английский

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Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)

Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 226, P. 107161 - 107161

Published: Sept. 27, 2022

Language: Английский

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Machine Learning for Electronically Excited States of Molecules

Chemical Reviews, Journal Year: 2020, Volume and Issue: 121(16), P. 9873 - 9926

Published: Nov. 19, 2020

Electronically excited states of molecules are at the heart photochemistry, photophysics, as well photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but branch artificial intelligence can be used advance exciting research field all its aspects. Discussed applications for include dynamics simulations, static calculations absorption spectra, many others. order put these studies into context, discuss promises pitfalls involved techniques. Since latter mostly based chemistry provide short introduction electronic structure methods approaches nonadiabatic describe tricks problems when using them molecules.

Language: Английский

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Artificial intelligence in drug discovery: recent advances and future perspectives

Expert Opinion on Drug Discovery, Journal Year: 2021, Volume and Issue: 16(9), P. 949 - 959

Published: March 29, 2021

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep multiple scientific disciplines, and the advances computing hardware software, among other factors, continue to fuel this development. Much initial skepticism regarding applications AI pharmaceutical discovery started vanish, consequently benefitting medicinal chemistry.Areas covered: current status chemoinformatics is reviewed. topics discussed herein include quantitative structure-activity/property relationship structure-based modeling, de novo molecular design, chemical synthesis prediction. Advantages limitations learning are highlighted, together with a perspective on next-generation for discovery.Expert opinion: Deep learning-based approaches have only begun address some fundamental problems Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid innovative paradigms, will likely become commonplace help most challenging questions. Open data sharing model development play central role advancement AI.

Language: Английский

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Transfer Learning for Drug Discovery

Journal of Medicinal Chemistry, Journal Year: 2020, Volume and Issue: 63(16), P. 8683 - 8694

Published: July 16, 2020

The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled is a major barrier artificial-intelligence-assisted discovery. One solution this problem develop algorithms that can cope with relatively heterogeneous and scarce data. Transfer learning type machine leverage existing, generalizable knowledge from other related tasks enable separate task small set Deep transfer most commonly used field This Perspective provides an overview applications date. Furthermore, it outlooks on future development

Language: 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, Journal Year: 2020, Volume and Issue: 41(3), P. 1427 - 1473

Published: Dec. 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.

Language: Английский

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