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!!