Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

By Xin Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, Sheng-yong Yang. Chemical Reviews, 2019

768 citations recorded by the source corpus.

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f8b012720a2322dcf4ed9ac4d61d6be11d9ebd10

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Citations in the corpus

Artificial intelligence in drug discovery and development (2020, methodology): “Several web-based tools, such as LimTox, pkCSM, admetSAR, and Toxtree, are available to help reduce the cost [35].”
Artificial intelligence in drug development (2025, neutral): “As real-world and multi-omics data grow richer, utilizing advanced data mining algorithms and expert knowledge will further enhance their integration, significantly improving the success rate of target discovery. comprehensive reviews 2,24,25 .”
Rethinking drug design in the artificial intelligence era (2019, background): “The field is now in the midst of a surge of interest, catalysed by advances in data processing power and the development of new artificial intelligence (AI) tools 7,8 .”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “Deep learning approaches, that is, artificial neural networks with several hidden processing layers [4,5], have recently gathered renewed attention owing to their ability to perform automatic feature extractions from the input data, and their potential to capture nonlinear input–output…”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “They also intend to establish a uniform data format, which is technically challenging [161].”
Computational approaches streamlining drug discovery (2023, methodology): “The advent of DL takes data-driven models to the next level, allowing analysis of much larger and diverse datasets while deriving more complicated non-linear relationships, with vast literature describing specific DL methodologies and applications to drug discovery 27,70 .”