Meta-analysis and review of in silico methods in drug discovery - part 1: technological evolution and trends from big data to chemical space
Abstract
This review offers an overview of advanced in silico methods crucial for drug discovery, emphasizing their integration with data science, and investigates the effectiveness of data science, machine learning, and artificial intelligence via a thorough meta-analysis of existing technologies. This meta-analysis aims to rank these technologies based on their applications and accessibility of knowledge. Initially, a search strategy yielded 900 papers, which were then refined into two subsets: the top 300 most-cited papers since 2000 and papers selected for systematic review based on high impact. From these, 97 articles were identified for discussion, categorized by their influence on society. The focus remains on the qualitative impact of these disciplines rather than solely on metrics like new drug approvals. Ultimately, the review underscores the role of big data in enhancing our comprehension of drug candidate trajectories from development to commercialization, utilizing information stored in publicly available databases to chemical space. Graphical extrapolation of some keywords (Drug Discovery; Big Data; Database; Metadata) discussed in this article and their evolution (in terms of absolute items that are available) by time.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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