3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry
Registration is open!!
- Event 3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry
- Dates Monday-Tuesday, 28th-29th September 2020
- Format A virtual event
- Organisers RSC BMCS and RSC CICAG (Royal Society of Chemistry’s Biological and Medicinal Chemistry Sector, and Chemical Information and Computer Applications Group)
- Websites https://www.maggichurchouseevents.co.uk/bmcs. Also https://www.rsc.org/events/detail/42785/3rd-rsc-bmcs-rsc-cicag-artificial-intelligence-in-chemistry
- Twitter #AIChem20
Artificial Intelligence is presently experiencing a renaissance in development of new methods and practical applications to ongoing challenges in Chemistry. Following the successes of two “Artificial Intelligence in Chemistry” meetings in 2018 and 2019, we are pleased to announce that the Biological & Medicinal Chemistry Sector (BMCS) and Chemical Information & Computer Applications Group (CICAG) of the Royal Society of Chemistry are once again organising a conference to present the current efforts in applying these new methods. The meeting will be held over two days and combine aspects of artificial intelligence and deep machine learning methods to applications in chemistry
The Call for Abstracts is Open.
Applications for both oral and poster presentations are invited. Posters will be displayed during a dedicated poster session and, at the time of submission, applicants are asked if they wish to provide a two-minute lightning oral presentation. The closing date for all submissions is Friday, 31st July.
There will be a mix of plenary and keynote talks as well as poster sessions with some lightning poster talks. There will also be exhibitor sessions, and we are currently exploring options for providing breakout rooms for discussions.
Teaching neural network to attach and detach electrons from molecules
Keynote: Olexandr Isayev, Carnegie Mellon, US
DNA-encoded small molecules libraries meet machine learning
Keynote: Patrick Riley, Google, US
Artificial neural network enhanced synthesis and retrosynthesis prediction
Esben Jannik Bjerrum, AstraZeneca, SE
Machine learning for free energy calculations
Hannah Bruce McDonald, Memorial Sloan Kettering Cancer Center, US
Data driven representations for predicting molecular properties: benchmarking and applications in generative chemistry
Jessica Lanini, Novartis, CH
Using machine learning for molecular dynamics simulations
Sereina Riniker, ETH Zürich, CH
Participation is free of charge, although registration is required so we can ensure the numbers are covered by the virtual meeting software license:
- online via this link (no payment required) I