The 4th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry is now open for abstracts. Full details are here https://www.maggichurchouseevents.co.uk/bmcs/AI_2021.htm. This is always a highly popular event.
This will be a virtual event and held Monday-Tuesday, 27th-28th September 2021. The first few confirmed speakers have been announced and there is already an interesting spread of topics.
AI for molecular design, past, present and future, Ola Engkvist, AstraZeneca, SE
Exploring molecular space and accelerating drug discovery with MegaMolBART, a transformer-based generative model, Michelle Gill, NVIDIA, US
Presentation title to be confirmed, Pat Walters, Relay Therapeutics, US
You can register online here https://www.maggichurchouseevents.co.uk/bmcs/AI-2021onlineregistration.htm.
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|
|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|
|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|
|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
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.
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
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