Version 1
: Received: 7 May 2024 / Approved: 9 May 2024 / Online: 10 May 2024 (10:15:20 CEST)
How to cite:
Uzundurukan, A.; Nelson, M.; Teske, C.; Islam, M. S.; Mohamed, E.; Christy, J. V.; Martin, H.-J.; Muratov, E.; Glover, S.; FUOCO, D. Meta-Analysis and Review of in silico Methods in Drug Discovery – Part 1: Technological Evolution and Trends from Big Data to Chemical Space. Preprints2024, 2024050601. https://doi.org/10.20944/preprints202405.0601.v1
Uzundurukan, A.; Nelson, M.; Teske, C.; Islam, M. S.; Mohamed, E.; Christy, J. V.; Martin, H.-J.; Muratov, E.; Glover, S.; FUOCO, D. Meta-Analysis and Review of in silico Methods in Drug Discovery – Part 1: Technological Evolution and Trends from Big Data to Chemical Space. Preprints 2024, 2024050601. https://doi.org/10.20944/preprints202405.0601.v1
Uzundurukan, A.; Nelson, M.; Teske, C.; Islam, M. S.; Mohamed, E.; Christy, J. V.; Martin, H.-J.; Muratov, E.; Glover, S.; FUOCO, D. Meta-Analysis and Review of in silico Methods in Drug Discovery – Part 1: Technological Evolution and Trends from Big Data to Chemical Space. Preprints2024, 2024050601. https://doi.org/10.20944/preprints202405.0601.v1
APA Style
Uzundurukan, A., Nelson, M., Teske, C., Islam, M. S., Mohamed, E., Christy, J. V., Martin, H. J., Muratov, E., Glover, S., & FUOCO, D. (2024). Meta-Analysis and Review of <em>in silico</em> Methods in Drug Discovery – Part 1: Technological Evolution and Trends from Big Data to Chemical Space. Preprints. https://doi.org/10.20944/preprints202405.0601.v1
Chicago/Turabian Style
Uzundurukan, A., Samantha Glover and DOMENICO FUOCO. 2024 "Meta-Analysis and Review of <em>in silico</em> Methods in Drug Discovery – Part 1: Technological Evolution and Trends from Big Data to Chemical Space" Preprints. https://doi.org/10.20944/preprints202405.0601.v1
Abstract
BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.
Keywords
Data science; Big data; Data mining; Bioinformatics; Chemometric; Medicinal chemistry; Targets; Knowledge discovery; Artificial intelligence, Machine learning, Deep learning; Data integration; Metadata, Database; QSAR; Collaborative drug discovery; Structure-based drug design; Ligand-based drug discovery; Clinical trials; Product development.
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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