The discussions reflect strong support and fascination with the article, as exemplified by references to its revolutionary speed and accuracy improvements, with phrases like 'wow!' and 'game changer' indicating excitement. The tone emphasizes the significance of integrating AI into chemistry, and the use of technical hashtags and mentions underscores the high level of engagement and perceived importance.
AgreementModerate agreement
Most discussions clearly express support and highlight the innovative aspects of the publication, especially emphasizing its potential to revolutionize chemical synthesis.
InterestHigh interest
The posts demonstrate high interest by referencing specific applications in machine learning, AI, and med chemistry, showing engagement with the technology and its implications.
EngagementModerate engagement
Posts include re-tweets, mentions of Zoom meetings, and discussions about technical details, indicating an active and engaged audience that has deeply interacted with the work.
ImpactHigh impact
The wide sharing and enthusiastic tone, along with mentions of the publication's significant viewership and potential to influence research and industry practices, suggest a high perceived impact.
Abstract in brief
The study combines Monte Carlo tree search with deep neural networks to improve computer-aided retrosynthesis for organic molecules, making it faster and more accurate.
Neural networks guided the search process by predicting promising reactions and filtering candidates, trained on almost all published organic reactions.
The new system nearly doubles the number of molecules it can solve and is 30 times faster than traditional methods, with chemists rating the AI-generated routes as equivalent to expert-designed ones in blind tests.]
Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/kxmkTApksV #DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #DL #ML #DS #AI #DNN #NeuralNetworks #NLP #GPU #TensorFlow #Keras #Pytorch #Python #HPC #Automation #AutonomousCar
Nov 7, 2019 · 5 likes · 9 shares · 14,483 followersView post →
JM
Joshua Mylne
@j_mylne · X
Just got off a Zoom meeting with @PendingAI's Mark Waller ... wow! AI to generate ideas and guide synthesis for med chem. See https://t.co/GdIkmCzoLV "Planning chemical syntheses with deep neural networks and symbolic AI" @SMS_UWA
Aug 1, 2019 · 2 likes · 1,655 followersView post →
PA
Pending AI
@PendingAI · X
Planning chemical syntheses with deep neural networks and symbolic AI has been viewed 44k times already! https://t.co/8vJkgIva2C
#AIChem20 Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018, 555 (7698), 604-610.
Sep 29, 2020 · 1 like · 1,496 followersView post →
DP
Dr. Ganapathi Pulipaka
@gp_pulipaka · X
RT @gp_pulipaka: Chemical Synthesis With Artificial Intelligence: Researchers Develop New Computer Method: Planning Chemical Syntheses With…
Nov 7, 2019 · 20 shares · 154,930 followersView post →
DF
Data Governance Framework
@gdprAI · X
RT @Deep_In_Depth: Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/kxmkTApksV #DeepLearning #MachineLear…
Nov 7, 2019 · 9 shares · 9,032 followersView post →
Javier Garc�a discusses how automating experiments with robotic chemists can revolutionize material design and research efficiency, highlighting advancements like autonomous testing for photocatalysts and AI-driven…
This video explores how artificial intelligence and robotics are transforming chemistry, offering opportunities for sustainable development and advancements in chemical research. It highlights the use of autonomous…
#AIChem20 Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018, 555 (7698), 604-610.
Sep 29, 2020 · 1 like · 1,496 followersView post →
K
K
@k29924543 · X
ずっと気になっていた論文をざっと読みました。技術的な詳細はフォローできませんでしたが、、、
Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/e7O1ZTwPcA
RT @Deep_In_Depth: Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/kxmkTApksV #DeepLearning #MachineLear…
Nov 7, 2019 · 9 shares · 9,032 followersView post →
W
WioNavi
@wiomax_cn · X
RT @Deep_In_Depth: Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/kxmkTApksV #DeepLearning #MachineLear…
Nov 7, 2019 · 9 shares · 2,040 followersView post →
DD
Deep_In_Depth
@Deep_In_Depth · X
Planning chemical syntheses with deep neural networks and symbolic AI https://t.co/kxmkTApksV #DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #DL #ML #DS #AI #DNN #NeuralNetworks #NLP #GPU #TensorFlow #Keras #Pytorch #Python #HPC #Automation #AutonomousCar
Nov 7, 2019 · 5 likes · 9 shares · 14,483 followersView post →
JM
Joshua Mylne
@j_mylne · X
Just got off a Zoom meeting with @PendingAI's Mark Waller ... wow! AI to generate ideas and guide synthesis for med chem. See https://t.co/GdIkmCzoLV "Planning chemical syntheses with deep neural networks and symbolic AI" @SMS_UWA
Aug 1, 2019 · 2 likes · 1,655 followersView post →
PA
Pending AI
@PendingAI · X
Planning chemical syntheses with deep neural networks and symbolic AI has been viewed 44k times already! https://t.co/8vJkgIva2C
Planning Chemical Syntheses With Deep Neural Networks and ...
News
Article presentation by Benjamin Heuser, Nature (29 March 2018). Frank de Morrée and Iestyn Watkin. doi:10.1038/nature25978. Outline The Problem. Methods.