Automating drug discovery

By Gisbert Schneider. Nature reviews. Drug discovery, 2017

721 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1256aaed3d89a81a2914e11b7b53757fbc75b2cf

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In Biomedical Engineering

In Computational Theory and Mathematics

Citations in the corpus

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, methodology): “Several compounds based on the imidazopyridine scaffold have been synthesised using automated computer-assisted de novo design resulting in the discovery of many ligands for G protein-coupled receptors antagonists [213,235,236].”
A robotic platform for flow synthesis of organic compounds informed by AI planning (2019, background): “Synthesis is often a bottleneck in small-molecule drug discovery ( 1 ), where design – synthesize – test iter- ationshavecycletimesontheorderofweeksandwherethescopeofacompoundlibrarysynthe-sis can determine the accuracy of an empirical structure – activity relationship model ( 2 ).”
Automated Machine Learning: From Principles to Practices (2018, methodology): “Then we formulate the AutoML problem as a bi-level optimization problem, propose the general learning strategy, and provide theoretical interpretation of AutoML.”
Rethinking drug design in the artificial intelligence era (2019, methodology): “…effectiveness of the DMTA cycle (Box 4) , for example, with the greater use of predicted data 123 , improved data analysis tools 124 and increased effectiveness in compound synthesis 125,126 , thereby shortening the timelines for the delivery of the critical data to address a hypothesis 127–130 .”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “The other uses of AI in drug development include the prediction of feasible synthetic routes for drug-like molecules [32], pharmacological properties [33], protein characteristics as well as efﬁcacy [34], drug combination and drug–target association [35] and drug repurposing [36].”
Artificial intelligence in drug development (2025, neutral): “The schematic diagram illustrates the seamless integration of AI-driven software with experimental execution and result analysis within the automated synthesis process.”
Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020, methodology): “Adaptive OED algorithms have been successfully applied to physical science domains, such as materials science 1,2,12–14 , chemistry 15,16 , biology 17 and drug discovery 18 , as well as to computer science domains, such as hyperparameter optimization for machine learning 19,20 .”
Combining generative artificial intelligence and on-chip synthesis for de novo drug design (2020, background): “INTRODUCTION Rapid iteration of the molecular design-make-test-analyze (DMTA) cycle has the potential for making “better decisions faster” (1, 2), with numerous applications in drug discovery and related fields (3, 4).”