Skip to main content

Meeting: 20th European Symposium on Quantitative Structure-Activity Relationships (EuroQSAR-2014), St. Petersburg



EuroQSAR-2014 will be held in St.-Petersburg, Russia on August 31st - September 4th, 2014. The deadline for oral talks' abstracts submission to the EuroQSAR-2014 is April 23rd, 2014. The meeting, entitled Understanding Chemical-Biological Interactions, will include 9 plenary lectures and 28 oral communications, which will be selected from the submitted abstracts and will focus on:
  • Chemical-Biological Space: Representation, Visualisation and Navigation.
  • Chemo- and Bioinformatics Approaches to Multi-Target (Q)SAR.
  • Modeling of Protein-Ligand Interactions: Structure, Function and Dynamics.
  • Assessing Ligand Binding Kinetics.
  • Computational Toxicology in Drug and Chemical Safety Assessment.
  • Translational Bioinformatics: From Genomes to Drugs.
  • Emerging QSAR and Modeling Methods.
Two seminars/roundtables are also planned on the last day of the Symposium:
  • (Q)SAR-Related European Initiatives.
  • Employing Proper Statistical Approaches for QSAR Modeling and Best Publishing Practices.
Confirmed speakers include:
  • Opening Lecture - SAR, the Lifelong Learning for my Career Prof. Toshio FUJITA (KYOTO UNIVERSITY, Kyoto, Japan)
  • From QSAR to MQSPR and Beyond: Predictive Materials Informatics Using a Blend of Heuristic and Physics-Based Methods
  • Prof. Curt BRENEMAN (RENSSELAER EXPLORATORY CENTER FOR CHEMINFORMATICS RESEARCH, Troy, United States)
  • Integrating Pharmacometrics into Drug Development Dr Roberta BURSI (GRÜNENTHAL, Aachen, Germany)
  • Lead Discovery and Optimisation by Use of Interaction Kinetic Analysis Prof. Helena DANIELSON (UPPSALA UNIVERSITY, Uppsala, Sweden)
  • Navigation in Chemical Space Towards Biological Activity Dr Peter ERTL (NOVARTIS INSTITUTE FOR BIOMEDICAL RESEARCH, Basel,
  • Switzerland)
  • Computational Toxicology – An Essential Part of Drug Safety Dr Catrin HASSELGREN (ASTRAZENECA, Mölndal, Sweden)
  • Ensemble-Based Drug Design, Combining Protein Structures and Simulations Dr Will PITT (UCB PHARMA, Slough, United Kingdom)
  • The Metabolic Code
  • Prof. Brian SHOICHET (UNIVERSITY OF TORONTO, Toronto, Canada)
  • Closing Lecture - Opportunities and Challenges in Therapeutics Discovery and Development Dr John C. REED (F. HOFFMAN-LA-ROCHE, Basel, Switzerland)
Hansch Session

  • On the Nature of Non-Classical Hydrogen Bonds and Aromatic Interactions Prof. Anna LINUSSON (UMEA UNIVERSITY, Umea, Sweden)
  • Lessons Learned from the Invention of QSAR Can Inspire Other Breakthrough Discoveries Dr Yvonne C. MARTIN (MARTIN CONSULTING, Waukegan, United States)
  • The Road Ahead: New Challenges for Computational Forecasts Prof. Tudor I. OPREA (UNIVERSITY OF NEW MEXICO, Albuquerque, United States)
  • Molecular Design of Bivalent and Dual Action Drugs Prof. Nikolay S. ZEFIROV (MOSCOW STATE UNIVERSITY, Moscow, Russia)
Proceedings of the Symposium will be published in a special issue of the journal Molecular Informatics.

More information you may find at the Symposium’s web-site: www.euroqsar2014.org

Comments

Popular posts from this blog

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! 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 f

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

LSH-based similarity search in MongoDB is faster than postgres cartridge.

TL;DR: In his excellent blog post , Matt Swain described the implementation of compound similarity searches in MongoDB . Unfortunately, Matt's approach had suboptimal ( polynomial ) time complexity with respect to decreasing similarity thresholds, which renders unsuitable for production environments. In this article, we improve on the method by enhancing it with Locality Sensitive Hashing algorithm, which significantly reduces query time and outperforms RDKit PostgreSQL cartridge . myChEMBL 21 - NoSQL edition    Given that NoSQL technologies applied to computational chemistry and cheminformatics are gaining traction and popularity, we decided to include a taster in future myChEMBL releases. Two especially appealing technologies are Neo4j and MongoDB . The former is a graph database and the latter is a BSON document storage. We would like to provide IPython notebook -based tutorials explaining how to use this software to deal with common cheminformatics p

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra

ChEMBL 26 Released

We are pleased to announce the release of ChEMBL_26 This version of the database, prepared on 10/01/2020 contains: 2,425,876 compound records 1,950,765 compounds (of which 1,940,733 have mol files) 15,996,368 activities 1,221,311 assays 13,377 targets 76,076 documents You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site . Please see ChEMBL_26 release notes for full details of all changes in this release. Changes since the last release: * Deposited Data Sets: CO-ADD antimicrobial screening data: Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.601