Bioinformatics Training Course course KDMC11, July 12th

Bioinformatics Training Course course KDMC11, July 12th - 15th, 2011, Portugal

There is an interesting course being run in Oeiras, Portugal in July.

About one hundred million different chemical compounds have already been synthesized. The number of theoretically possible organic molecules exceeds the number of atoms in the universe. This raises a number of questions, including:

Where can one find information about chemical structures and their properties?
How can one efficiently retrieve such information?
Which molecules, if synthesized, could potentially assist the fight against certain types of cancer?
Why is it that some pharmacological targets are considered more promising for the prevention or treatment of Alzheimer?s disease than others?
Are there ways to better predict ADME(T) properties of synthesized molecules?
Why does such a significant proportion of launched drugs originate from structures found in natural products?
Why can multi-target pharmacological agents be superior to single-targeted ones?
Can new medical applications be found for old drugs?
Why is the alliance of chemo- and bioinformatics beneficial to the life sciences, biotech and pharma industries?
What are the major challenges facing chemoinformatics now?

During this course participants will learn how to efficiently find answers to these and many other related questions. Attendees will be instructed in the use of the relevant databases and associated software to:

Represent compounds and (bio)chemical reactions using chemical information in a computer.
Search for information about chemical structures and their properties in public and commercially available databases.
Perform similarity searches with an understanding of the advantages and disadvantages of the various methods.
Prepare data sets for further (Q)SAR/(Q)SPR analysis, estimating the quality and completeness of the data.
Create and validate (Q)SAR/(Q)SPR models for finding and optimization of lead compounds.
Use the above techniques for virtual screening and design of chemical compounds with the required properties.

Target audience
Researchers working in life sciences, professionals in the pharmaceutical and biotech industries: organic, medicinal, pharmaceutical chemists, biochemists, molecular biologists, pharmacologists, toxicologists, and others.

Information on all GTPB courses can be found at http://gtpb.igc.gulbenkian.pt

Comments

Improvements in SureChEMBL's chemistry search and adoption of RDKit

Dear SureChEMBL users, If you frequently rely on our "chemistry search" feature, today brings great news! We’ve recently implemented a major update that makes your search experience faster than ever. What's New? Last week, we upgraded our structure search engine by aligning it with the core code base used in ChEMBL . This update allows SureChEMBL to leverage our FPSim2 Python package , returning results in approximately one second. The similarity search relies on 256-bit RDKit -calculated ECFP4 fingerprints, and a single instance requires approximately 1 GB of RAM to run. SureChEMBL’s FPSim2 file is not currently available for download, but we are considering generating it periodicaly and have created it once for you to try in Google Colab ! For substructure searches, we now also use an RDKit -based solution via SubstructLibrary , which returns results several times faster than our previous implementation. Additionally, structure search results are now sorted by

ChEMBL brings drug bioactivity data to the Protein Data Bank in Europe

In the quest to develop new drugs, understanding the 3D structure of molecules is crucial. Resources like the Protein Data Bank in Europe (PDBe) and the Cambridge Structural Database (CSD) provide these 3D blueprints for many biological molecules. However, researchers also need to know how these molecules interact with their biological target – their bioactivity. ChEMBL is a treasure trove of bioactivity data for countless drug-like molecules. It tells us how strongly a molecule binds to a target, how it affects a biological process, and even how it might be metabolized. But here's the catch: while ChEMBL provides extensive information on a molecule's activity and cross references to other data sources, it doesn't always tell us if a 3D structure is available for a specific drug-target complex. This can be a roadblock for researchers who need that structural information to design effective drugs. Therefore, connecting ChEMBL data with resources like PDBe and CSD is essen

Improved querying for SureChEMBL

Dear SureChEMBL users, Earlier this year we ran a survey to identify what you, the users, would like to see next in SureChEMBL. Thank you for offering your feedback! This gave us the opportunity to have some interesting discussions both internally and externally. While we can't publicly reveal precisely our plans for the coming months (everything will be delivered at the right time), we can at least say that improving the compound structure extraction quality is a priority. Unfortunately, the change won't happen overnight as reprocessing 167 millions patents takes a while. However, the good news is that the new generation of optical chemical structure recognition shows good performance, even for patent images! We hope we can share our results with you soon. So in the meantime, what are we doing? You may have noticed a few changes on the SureChEMBL main page. No more "Beta" flag since we consider the system to be stable enough (it does not mean that you will never

ChEMBL 34 is out!

We are delighted to announce the release of ChEMBL 34, which includes a full update to drug and clinical candidate drug data. This version of the database, prepared on 28/03/2024 contains: 2,431,025 compounds (of which 2,409,270 have mol files) 3,106,257 compound records (non-unique compounds) 20,772,701 activities 1,644,390 assays 15,598 targets 89,892 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/ Please see ChEMBL_34 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/chembl_34_release_notes.txt New Data Sources European Medicines Agency (src_id = 66): European Medicines Agency's data correspond to EMA drugs prior to 20 January 2023 (excluding vaccines). 71 out of the 882 newly added EMA drugs are only authorised by EMA, rather than from other regulatory bodies e.g.

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

The ChEMBL-og

Search This Blog