The lineup for the 5th Artificial Intelligence in Chemistry Symposium (Thursday-Friday, 1st-2nd September 2022) is now complete for both oral and poster presentations. It really is a fantastic selection of topics and speakers and it is clear this event is now a highlight of the scientific calendar.

The details are here https://www.rscbmcs.org/events/aichem22/

Registration is now open, register here https://www.eventsforce.net/hg3/221/home. In person registration deadline: Monday 29th August 17:00 (BST)

 

The 5th Artificial Intelligence in Chemistry is now open for both oral and poster abstract submission. This meeting will held at Churchill College 1-2 September 2022. #AIChem22

Confirmed Speakers Include

Charlotte Deane, Connor Coley, Kim Jelfs, Val Gillet, Adrian Roitberg,

You can submit your abstracts here https://hg3.co.uk/ai/.

The circular for the meeting is here https://www.rscbmcs.org/wp-content/uploads/2022/01/FINALAIinChemistry1st_Announcement.pdf

 

An updated program for the 4th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Meeting is now available and there is the usual excellent range of speakers. Registration is now open https://www.maggichurchouseevents.co.uk/bmcs/AI-2021onlineregistration.htm. There are also opportunities for sponsorship.

Confirmed Speakers

Keynote: AI for molecular design, past, present and future Ola Engkvist, AstraZeneca, SE

Keynote: Challenges and opportunities for machine learning in drug discovery Patrick Walters, Relay Therapeutics, US

PyPEF – an integrated framework for data-driven protein design and engineering Mehdi Davari, Leibniz Institute of Plant Biochemistry (IPB), DE

Efficient ML strategies to explore chemical reactivity Fernanda Duarte, University of Oxford, UK

Exploring molecular space and accelerating drug discovery with MegaMolBART, a transformer-based generative model Michelle Gill, NVIDIA, US

Machine learning models for predicting human in vivo PK parameters using chemical structure and dose Olga Obrezanova, AstraZeneca, UK

Molecular transformer-aided biocatalysed synthesis planning Daniel Probst, IBM Research Europe, CH

Best practice for chemical language model de novo design of GPCR ligands: datasets, scoring functions and optimization algorithms Morgan Thomas, University of Cambridge, UK

Presentation title to be confirmed (provisional) Andrea Volkamer, Charité Universitätsmedizin Berlin, DE

‘Attending’ to co-crystals in the Cambridge Structural Datacenter Aikaterini Vriza, University of Liverpool, UK

Driving lead optimisation with BRADSHAW Ian Wall, GlaxoSmithKline, UK

 

The heavily oversubscribed 3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry will taking place next week 28th-29th September 2020 Twitter hashtag - #AIChem20. There is an accompanying poster session and there is a chance to talk to the poster presenters in the breakout rooms at the end of each day (You will need the latest version of Zoom 5.3.0).

Most of the posters are now available for viewing on Twitter so you can always have a browse and ask questions on Twitter even if you won't be at the meeting #AIChem20poster,

Below is a table containing all posters.

Poster Number	Name	Title	Twitter link
P01	Antreas Afantitis	Enalos cheminformatics tools: development of a de novo drug design module	View on twitter
P02	Nurlybek Amangeldiuly	Transfer learning with graph neural networks for protein_ligand binding kinetics prediction	View on twitter
P03	Andy Sode Anker	Characterising the atomic structure of mono_metallic nanoparticles from x_ray scattering data using conditional generative models	View on twitter
P04	Jenna Bilbrey	A look inside the black box: using graph_theoretical descriptors for the post_hoc interpretation of neural networks	View on twitter
P05	Nicolas Bosc	MAIP: a prediction platform for predicting blood_stage malaria inhibitors	View on twitter
P06	Xiaojing Cong	Receptor_ligand prediction by proteochemometric modeling: an application to G protein_coupled olfactory receptors	View on twitter
P07	Simon Durr	EVOLVE: a genetic algorithm to predict thermostability	View on twitter
P08	Umberto Esposito	Building a connected data pipeline to target drug development challenges
P09	Benedek Fabian	MolBERT: molecular representation learning with advanced language models and useful auxiliary tasks	View on twitter
P10	Miguel Garcia_Ortegon	Improving VAE molecular representations by tailoring them to predict docking poses and scores	View on twitter
P11	Wenhao Gao	Can we synthesize molecules proposed by generative models	View on twitter
P12	Helena Gaspar	Proteochemometric models using multiple sequence alignments and a SentencePiece_based masked language model: application to CYP and kinome selectivity modelling	View on twitter
P13	Ed Griffen	An explainable AI system for medicinal chemists	View on twitter
P14	Ed Griffen	"Chemists: AI is here, unite to get the benefits"	View on twitter
P15	Thomas Hadfield	Explicit incorporation of structural information into a fragment elaboration model via deep reinforcement learning	View on twitter
P16	Hans Hanley	"GENerateZ: designing anticancer drugs using transcriptomic data, genetic algorithms, and variational autoencoder"	View on twitter
P17	Fergus Imrie	Generating property_matched decoy molecules using deep learning	View on twitter
P18	Kjell Jorner	Uniform quantitative predictive modelling for route design	View on twitter
P19	Itai Levin	Computationally assisted synthesis planning for hybrid chemoenzymatic pathways	View on twitter
P20	Timur Madzhidov	Deep conditional variational autoencoder for reaction conditions prediction	View on twitter
P21	Gergely Makara	AI_assisted lead optimization with derivatization design	View on twitter
P22	Neann Mathai	Performance and scope of a similarity_based and a random forest_based machine learning approach for small_molecule target prediction	View on twitter
P23	Janosh Menke	Enhancing molecular fingerprints using neural networks	View on twitter
P24	Juan Carlos Mobarec	Evolutionary chemistry for the design of desired pharmacological profiles	View on twitter
P25	Rohit Modee	Neural network potentials for representing potential energy surface and their applicability for geometry optimization	View on twitter
P26	Joseph Morrone	Challenges and progress in combining docking programs with deep neural networks	View on twitter
P27	Eva Nittinger	Non_additivity in public and inhouse data and its influence on ML performance	View on twitter
P28	Ferruccio Palazzesi	Integrating multi task graph convolutional neural network with a deep generative model	View on twitter
P29	Yashaswi Pathak	Deep learning enabled inorganic material generator	View on twitter
P30	Quentin Perron	Integrating data_driven computer_aided synthetic planning with generative AI
P31	Daniel Probst	Classification of chemical reactions through NLP_inspired fingerprinting	View on twitter
P32	Mikolaj Sacha	Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits	View on twitter
P33	withdrawn
P34	Jenke Scheen	Data_driven estimation of optimally_designed perturbation networks in relative alchemical free energy calculations	View on twitter
P35	Philippe Schwaller	RXNMapper: unsupervised attention_guided atom_mapping	View on twitter
P36	Matthew Segall	Imputation versus prediction and applications in drug discovery	View on twitter
P37	Vishnu Sresht	Can generative models learn privileged substructures and identify new bioisosteres?	View on twitter
P38	Gergely Takács	Analysis of commercial and public compound databases by self_organizing maps	View on twitter
P39	Morgan Thomas	Towards integrating deep generative models with structure_based design	View on twitter
P40	Hao Tian	What is hidden behind allostery? An integrated framework to decipher key components in AuLOV dimerization	View on twitter
P41	Alain Vaucher	Learning how to do chemical reactions from data	View on twitter
P42	Alexander van Teijlingen	Beyond tripeptides _ two_step active machine learning for very large datasets	View on twitter
P43	James Wallace	eApps – enabling a predict first culture for computational medicinal chemistry	View on twitter
P44	withdrawn
P45	Yuanqing Wang	Bayesian active drug discovery via deep graph kernel learning	View on twitter
P46	Robbie Warringham	DigitalGlassware: structuring and contextualising chemical outcomes for faster discovery	View on twitter
P47	withdrawn
P48	Jerome Wicker	AIScape: a machine learning platform for activity and ADME predictions	View on twitter

Some of the presenters have also recorded 2 min lightning presentation describing their work, these are available on the RSC CICAG YouTube channel.

Day 1 (odd numbered posters) https://youtu.be/ikbrrnjOvdc

Day 2 (even numbered posters) https://youtu.be/tf8cGodlWfU

Enjoy!!

 

The heavily oversubscribed 3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry will taking place next week 28th-29th September 2020 Twitter hashtag - #AIChem20.

This is now an online virtual event, this has required an immense amount of reorganisation behind the scenes and significant expenditure. This would not have been possible without the generous support of the event sponsors. AstraZeneca and MSD and the exhibitors CCDC, Concept Life Sciences, IKTOS, Liverpool ChiroChem, Mcule, o2h discovery.

Some of the exhibitors have kindly provided videos describing their work, why not have a browse.

IKTOS

Liverpool ChiroChem

Mcule

o2h discovery

If you are at the meeting they will also be able to talk to you directly at the breakout session at the end of each day.

 

The heavily oversubscribed 3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry will taking place 28th-29th September 2020 Twitter hashtag - #AIChem20. This has been converted to a virtual event and those registered will be getting more details soon.

There is an accompanying poster session, the posters are being hosted on twitter and you can see them all using the hashtag AIChem20posters. A number of the poster presenters have also created 2 min lightning presentations which will be uploaded to YouTube once the last few presentations have been included.

If you are registered and find you can no longer attend, please send an e-mail to the BMCS Secretariat promptly, so that your place can be allocated to one of those on the waiting list.

 

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

Synopsis.
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.

Confirmed Speakers
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

Registration 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

AI - second announcement