Artificial intelligence in drug development: present status and future prospects.

By Kit-Kay Mak, M. Pichika. Drug Discovery Today, 2019

704 citations recorded by the source corpus.

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.

Subfields: Computational Theory and Mathematics, Health Informatics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/41e11ad81e68ff301eba4ff216c63fc687b095fa

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In Computational Theory and Mathematics

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Citations in the corpus

A Structure-Based Drug Discovery Paradigm (2019, background): “Artificial intelligence (AI) mimics human behavior by simulating human intelligence by computer techniques [87].”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Hence, there is a dire need for new drug targets and drug compounds, which can alleviate the symptoms and mitigate the diseased conditions of the central nervous systems [462].”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, methodology): “For example, computational methods can reduce the time spent on examining data, predicting protein structures and genomes [115], [116].”
A Review of the Role of Artificial Intelligence in Healthcare (2023, background): “Repurposing a current drug passes it toward subsequent phases of clinical trials [100].”