Rethinking drug design in the artificial intelligence era

By Petra Schneider, W. Patrick Walters, A. Plowright, Norman Sieroka, Jennifer Listgarten, Robert A. Goodnow, Jasmin Fisher, Johanna M. Jansen, José S. Duca, Thomas Rush III and 8 more. Nature reviews. Drug discovery, 2019

681 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bd15d635a8b4c4435b01045981797fcd4b5671ef

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Citations in the corpus

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, background): “The main reason behind this fact is the vast chemical and biological datasets utilized to generate AI algorithms focused on elucidating structure–activity relationships, minimizing the cost and the time for lengthy experimental validation [33].”
Artificial intelligence in drug development (2025, positive): “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 .”
Drug discovery with explainable artificial intelligence (2020, background): “56 While the exact deﬁnition of XAI is still under debate, 41,57 several aspects of XAI are certainly desirable in drug design applications: 37 (i) transparency , i.e., knowing how the system reached a particular answer, (ii) justiﬁ-cation , i.e., elucidating why the answer provided by the model is…”
Computational approaches streamlining drug discovery (2023, background): “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 .”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “The AI-based tools developed for predicting bio-physical and biochemical properties of compounds include molecular fingerprinting, a SMILES format, Coulomb matrices, and potential energy measurements, which are used in the DNN training phase [299, 300].”
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, background): “[5] A number of synthetic methods for the selective activation and modification of C–H bonds allow for late-stage functionalization (LSF) of organic scaffolds, ranging from molecular building blocks to advanced drug molecules.”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “However, whether these techniques will actually prove useful by aiding researchers to design and synthesize ‘better drug candidates faster’ still remains to be demonstrated [188,189].”
Tree-structured Parzen estimator: Understanding its algorithm components and their roles for better empirical performance (2023, background): “Introduction In recent years, the complexity of experiments has seen a substantial upsurge, reflecting the rapid advancement of various research fields such as drug discovery (Schneider et al., 2020), material discovery (Xue et al.”
Explainable Machine Learning for Scientific Insights and Discoveries (2019, methodology): “For the successful application of ML for drug design, [114] identifies five ‘‘grand challenges’’: obtaining”