介绍:
Podcast Series Title:"Frontiers of AI Chemistry: Expert Insights"
Target Audience:Chemistry researchers, computational scientists, AI researchers interested in chemistry, and those in related fields.
Overall Series Goal:To provide a deep dive into the applications of Artificial Intelligence in Chemistry, exploring current advancements, challenges, and future directions through expert interviews and discussions.
Number of Episodes:7 episodes (as previously determined).
Podcast Series Outline:(Episodes 1-2 and 4-7 remain the same as previously outlined)
Episode 3: "Unlocking Reaction Efficiency: AI for Yield Prediction in Organic Synthesis" (Revised)
Host:Dr. Rizvi Syed Aal E Ali
Experts:
Dr. Paul Schwaller (Mentioned for BERT-based Yield Prediction):Introduce as expert in applying Natural Language Processing techniques to chemistry and reaction yield prediction, known for BERT-based models.
Dr. Allison M. Zuranski (First author on Doyle Group Yield Prediction paper):Introduce as expert in reaction optimization and machine learning for yield prediction, representing the Doyle group's supervised learning approach.
Episode Length:30-40 minutes (Content ~20 minutes)
Content Outline:
(Host Intro):Recap Episode 2, introduce topic: Yield Prediction, Experts: Dr. Schwaller and Dr. Zuranski.
(Expert 1 - Dr. Schwaller - 10 mins):
Host Question:"Dr. Schwaller, your work applied BERT models, typically used in NLP, to predict reaction yields. Can you explain this novel approach and how language-based models can capture chemical information from reaction SMILES for yield prediction?"
Expert Share:(Focus on BERT models, language analogy, rxnfp, strengths/limitations of language models for chemistry, referencing Schwaller et al. cite [73])
(Expert 2 - Dr. Zuranski - 10 mins):
Host Question:"Dr. Zuranski, representing the Doyle group, your work utilizes supervised learning for yield prediction. Can you detail the machine learning approaches, particularly supervised learning with descriptors, that are most effective for predicting reaction yields, drawing upon the Doyle group's work on Buchwald-Hartwig amination and Suzuki-Miyaura coupling?"
Expert Share:(Focus on supervised learning, descriptor-based models, Doyle group's work, HTS data, strengths/limitations of supervised learning with descriptors for yield prediction, referencing Doyle group cite [71, 74])
(Discussion QA - 10-15 mins):
Host Question to both:"Comparing BERT-based and descriptor-based approaches, what are the key advantages and disadvantages of each for yield prediction in organic synthesis?" (Compare and contrast two approaches).
Host Question to both:"What are the critical data requirements for training robust and accurate yield prediction models, regardless of the ML approach used?" (Data quality and quantity needs).
Host Question to both:"Looking ahead, what are the most promising future directions for AI-driven yield prediction in organic synthesis?" (Future directions for yield prediction).
(Host Outro):Summarize key takeaways, thank experts, tease next episode: Selectivity Prediction.
文中人名原文以及工作出处原文:
保罗·施瓦勒博士 (Dr. Paul Schwaller):
原文人名:P. Schwaller
工作出处原文 (Reference [73]):P. Schwaller, A.C. Vaucher, T. Laino, J.L. Reymond, Prediction of chemical reaction yields using deep learning, Mach. Learn. -Sci. Technol. 2 (2021) 015016,https://doi.org/10.1088/2632-2153/abc81d.
艾莉森·M·祖兰斯基博士 (Dr. Allison M. Zuranski):
原文人名:A.M. Zuranski
工作出处原文 (Reference [74]):A.M. Zuranski, J.I. Martinez Alvarado, B.J. Shields, A.G. Doyle, Predicting reaction yields via supervised learning, Acc. Chem. Res. 54 (2021) 1856-1865,https://doi.org/10.1021/acs.accounts.0c00770. (请注意，Zuranski博士是第一作者，通讯作者是A.G. Doyle，即Abigail G. Doyle教授)
大家还在听
