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Cited by 347Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

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

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Sarkar, Chayna, Das, Biswadeep, Rawat, Vikram Singh, Wahlang, Julie Birdie, Nongpiur, Arvind, Tiewsoh, Iadarilang, Lyngdoh, Nari M., Das, Debasmita +2 more·2023·International Journal of Molecular Sciences
DOI 10.3390/ijms24032026PMC PMC9916967
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Abstract

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

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  • Other reviews have focused on the perspectives of healthcare professionals on topics not directly involving patient care, such as research and drug development.

    “Other reviews have focused on the perspectives of healthcare professionals on topics not directly involving patient care, such as research and drug development [13, 14].”

    From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [14]

    The Introduction describes a review of drug research and development, and Section 7 discusses AI’s potential in pharmaceutical development. Those passages support the drug-development topic, but they do not show that the review focuses on healthcare professionals’ perspectives. (LLM, conf=0.85)

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Authors
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T.
Affiliations
  • North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (IN)
  • All India Institute of Medical Sciences Rishikesh (IN)
  • Vellore Institute of Technology University (IN)
Corresponding author
Das, Biswadeep
ORCID
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2023-01-19
Volume
24
Issue
3
Pages
2026
ISSN
1422-0067 (ISSN-L)
DOI
10.3390/ijms24032026
PMC
PMC9916967
OpenAlex
W4317625850
Cited by
347
References
243
Citations by year
2026: 96 · 2025: 117 · 2024: 101 · 2023: 33
FWCI
5.89
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in drug discovery, machine learning, deep learning, computational drug discovery, deep generative models, message passing networks, molecular descriptors, open data sharing
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Technology, Drug Design, Neural Networks, Computer, Drug Discovery

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