Machine Learning in Computer-Aided Synthesis Planning.
By Connor W. Coley, William H. Green Jr., Klavs F. Jensen. Accounts of Chemical Research, 2018
563 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/336d62ea5c8840afc7b8bf9fed801dabfe7daf7d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Gisbert Schneider
- Philippe Schwaller
- Kenneth Atz
- Rohit Batra
- Sepp Hochreiter
- Teodoro Laino
- A. Pais
- Tânia F Cova
In Materials Chemistry
- Philippe Schwaller
- Gisbert Schneider
- Rohit Batra
- Sepp Hochreiter
- A. Pais
- Tânia F Cova
- Rampi Ramprasad
- Philipp Renz
Citations in the corpus
- Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, background): “Moreover, a wide range of ML applications, including drug design (Ekins, 2016; Chen et al., 2018; Fleming, 2018), synthesis planning (Coley et al., 2018a), medicinal chemistry (Panteleev et al., 2018), cheminformatics (Lo et al., 2018), quantum mechanical calculations (Rupp, 2015), and materials…”
- Rethinking drug design in the artificial intelligence era (2019, methodology): “…data and thereby implicitly capture important aspects of chemical ‘synthesizability’. aI systems for explicit retrosynthetic analysis or synthesis planning can be used in concert with de novo design models 144–146 Examples of models used to generate de novo structures are shown in the figure.”
- A robotic platform for flow synthesis of organic compounds informed by AI planning (2019, background): “A renewed in- terestinCASPbroughtaboutbyrecentadvancesindatascienceandmachinelearninghasledtosev-eralrecentstudies( 27 , 28 – 30 ), including Segler et al .”
- Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (2018, background): “The long history of computational chemical reaction prediction has been extensively reviewed in [17] and [18].”
- Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “Recently, ML-based retrosynthesis planning algorithms 208 have been designed to tackle this problem with very promising results 209,210 .”
- Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, methodology): “[19–21] In combination with FAIR (Findability, Accessibility, Interoperability, Reusability) [22] documentation, which generates high-quality data sets on successful and failed reactions [23, 24], HTE provides a foundation to unlock LSF for drug discovery by enabling advanced data analysis and machi”