The next meeting is Hot Topics: Covalent Drug Discovery 2024, this online event is on Thursday 16th May, 2024 (afternoon).
Pharmaceutics plays a critical role in drug discovery however since it is often regarded as a development process it is not always given the prominence in basic research that it deserves. The pharmaceutical properties of a drug are absolutely critical in the success of a drug discovery project so I'm delighted to see a new open-access RSC journal on the topic
RSC Pharmaceutics has just published its first articles, https://pubs.rsc.org/en/journals/journalissues/pm#!recentarticles&adv.
Grant funding is a great way of starting of work on novel targets, getting funding to continue the work can be more of an issue. This why I'm delighted to read about the BBSRC follow-on fund to help bridge the gap.
FoF applications must draw substantially on current or prior BBSRC funding. You must be based at a UK research organisation eligible for BBSRC funding.
FoF awards aim to take ideas through to a stage where the route to practical application is clear.
The full economic cost (FEC) of your project can be up to £800,000. BBSRC will fund 80% of the FEC. FoF awards support defined programmes of work for up to two years.
One of the challenges when building novel tools to aid drug discovery is identifying high quality datasets that can be used to test new tools. This is why the D3R datasets are so valuable https://drugdesigndata.org.
These datasets are available from BindingDB and include a variety of important protein targets.
I hope you all have a great seasonal holiday, it has been a tough few years for folks so I think everyone needs a break. As Bad Company sang in Wishing Well.
But I know what you're wishing for, Love in a peaceful world
As in previous years rather post cards to everyone, instead any monies saved have been donated to the Multiple Sclerosis Society.
SureChEMBL is a database of automatically abstracted patents, it uses three different methods to get structures, name to structure, image to structure and for some patents mol files if available. If you use it regularly you will be aware that it has become somewhat unreliable and the performance is not ideal.
This has just changed with an updated version of SureChEMBL.
Almost 10 years ago, EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new features. We are now able to make available the first outputs from this project, which addresses the first two of these deliverables, with more to come in the future!
The new interface is here https://www.surechembl.org
If you have any issues you can submit them on GitHub https://github.com/chembl/surechembl-issues/issues.
One particularly useful new feature is the new public api. https://www.surechembl.org/api/swagger-ui.html. I'll certainly be exploring this in the future.
I've updated the Molecular Interactions page on the Drug Discovery Resources site.
Just catching up with my reading, I've always been a fan of compounds with slow off-rates and the impact on duration of action.
The situation was elegantly summarised in a publication from earlier in the year from Copeland et al. DOI
A dominant assumption in pharmacology throughout the 20th century has been that in vivo target occupancy-and attendant pharmacodynamics-depends on the systemic concentration of drug relative to the equilibrium dissociation constant for the drug-target complex. In turn, the duration of pharmacodynamics is temporally linked to the systemic pharmacokinetics of the drug. Yet, there are many examples of drugs for which pharmacodynamic effect endures long after the systemic concentration of a drug has waned to (equilibrium) insignificant levels. To reconcile such data, the drug-target residence time model was formulated, positing that it is the lifetime (or residence time) of the binary drug-target complex, and not its equilibrium affinity per se, that determines the extent and duration of drug pharmacodynamics.
I've added it to the page on separation of PK and PD in the Drug Discovery Resources
A recent publication describes the continued evolution of the AlphaFold Protein Structure Database created by EMBL-EBI and DeepMind. From an initial 300K structures it now contains 214 million predicted protein structures.
You can read the paper here DOI.
The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.
Just got details of this announcement of the latest person to be inducted into the Royal Society of Chemistry BMCS Hall of Fame.
The BMCS is delighted to announce that Professor Andrew Hopkins, FRS FMedSci FRSC, will be the 2023 inductee to its Hall of Fame, and the recipient of the associated medal.
Andrew is widely recognized for his seminal contributions to the use of informatics in drug design. His contributions have been highly impactful as evidenced by his highly cited papers in the area of Ligand Efficiency (LE), Chemical Beauty (QED) as well as the “druggable genome”. Andrew has been an early pioneer for the use of artificial intelligence (AI) and machine learning (ML) in drug discovery, and whilst it is a nascent field, it holds much potential for impact in the future. Combining his drug discovery expertise with his entrepreneurial spirit, Andrew founded and is leading, as Chief Executive, Exscientia plc, an AI-driven company dedicated to changing the way drugs are discovered. He led the teams that discovered the first drugs to enter human clinical trials, which were designed with the extensive use of ML and AI generative methods.
Andrew joins a fantastic group of individuals who have all made outstanding contributions to drug discovery.
Dr Karin Briner, Professor Dame Carol Robinson, Dr David Rees, Sir Simon Campbell, Professor C Robin Ganellin,