Last week, I participated RDKitUGM 2019! In this year, there were more than a hundred participants in the UGM. The meeting is growing year by year. The meeting repo is below and I think some presentation will be uploaded in few days ~ weeks I think.
And twitter has tag is #RDKitUGM2019.
Following document is a memorandum for myself. It is not fully cover the meeting topics and don’t describe details because some works are unpublished.
Floriane Montanari and Robin Winter: Utilizing in silico models in both directions: prediction and optimizing the properties of small molecules
- They used Swarm Optimization algorithm for molecular generation. The reference and code is available following URL.
- I couldn’t agree more about their opinion, ‘common sense of medicinal chemist is hard to model’ even if predictive and generative model worked well. ;-)
Mahendra Awale: SAR Transfer via Matched Molecular Series
- I thought MMS/MMP is useful for ADMET SAR transfer but difficult for bio activity SAR transfer.
- But he presented some examples for bio activity SAR transformation.
- MMS is easy to understand for MedChem compared to deep learning.
- It is very interesting approach for me.
Martin Vogt:Systematic extraction of analogue series from large compound collections
- Analogue Series(AS) is similar concept to MMP but there is difference in cutting rule. Default MMP cuts rotatable bond but AS cuts bond with RECAP.
Paul Czodrowski: Is bigger always better? Comparing two strategies for the generation of predictive models based on different computational resources
- New concept of fingerprint is described. And it was applied for molecular property prediction.
- He also shared excellent poster in github. Thanks!!!
Chaya Stern: Improving molecular models by generating high-quality quantum chemistry data
- Molecular modeling is failed for some reasons. And to solve the issue, they try to build high quality Force Field with quantum chemistry. I respect activity of the consortium.
Esben Bjerrum: Molecular De Novo Design – using Deep Learning Encoders and Generators together with RDKit
- Some years ago, AZ group published RNN based molecular generator named REINVENT. I think it is quite nice tool. And In the presentation, conditional RNN based generator is described.
- You can find his nice talk material following url. link
Dominique Sydow and Jaime Rodríguez-Guerra: TeachOpenCADD: An open source teaching platform for computer-aided drug design
- TeachOpenCADD is nice project for any chemoinformatitian.
- The team provides useful material for in silico drug discovery. You can find details in their article and some links.
Jan Halborg Jensen: Quantum chemistry meets cheminformatics
- Application of quantum chemistry is described. I had interested about reactivity prediction with QM.
- His cool work ‘RegioSQM’ for reactivity prediction is available from his github repo. https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc04156j#!divAbstract
Brian Kelley: "Learned" Molecule Representations – a technical comparison with data from real projects
- This talk is very useful for me. The way of molecular representations are described.
Christoph Bauer: Generation of Bimolecular 3D Complex Structures with RDKit
- QM based ML approach is described.
Suliman Sharif: Cocktail Shaker: An open source drug expansion and enumeration library using Python and RDKit
- New substituent shuffling tool named ‘cocktail-shaker’ is described. Fortunately, code is available from following URL.
Lighting talk and poster sessions
- There many interesting topics in these sessions.
- Such as rdkit-neo4j integration, rdkit-QM integration, MMPDB cloud funding etc…
And day3, I participated Knime Work shop because recent knime has lots of useful node. I could learn how to use knime in chemoinformatics.
I could have useful discussions with participants and enjoy not only the meeting but also food (of course beer too!), view of Hamburg.
I would like to say thank you all participants. I got a lot of energy and motivation from the UGM.