Download the full description of this project: ESR9: Fragment evolution platform - chemical navigation
Computer-‐aided drug design (CADD) approaches are able to generate accurate molecular models that integrate available structural data with biochemical and biophysical data. In this project, a computational platform will be established that will help to guide efficient fragment hit evolution.
Assemble the necessary cheminformatic infrastructure to generate, store and navigate chemical collections. Docking software will be an integral part of the system in order to exploit structural information, when available. 2. Identify chemical transformations in published fragment evolution programmes, assessing frequency and potency gain. 3. Create an algorithm capable of identifying an optimal list of evolved fragments for testing, given a fragment hit and optional additional information (e.g. list of compounds tested, structural information). 4. Use the platform in prospective FBDD projects, in collaboration with other ESRs.
Starting from existing software and computational methods developed in our lab, the ESR will develop a computational platform capable of suggesting an optimal set of molecules to test experimentally. The platform will accept as input a list of active and inactive fragments and, optionally, three-‐dimensional structures of the target. The ESR will generate robust and adaptable pipelines, combining computational packages in the fields of statistics, chemoinformatics, computational chemistry and others. This work will be carried out in close collaboration with the rest of the FRAGNET consortium to ensure that the final tool offers a practical solution in the majority of fragment-‐based scenarios. The ESR will also provide training to early adopters and apply the software to existing FBLD projects within FRAGNET.
Required diploma: MSc Bioinformatics or related degree and a background in chemistry, mathematics, pharmaceutical sciences or molecular life sciences. Required expertise: Strong programming and scripting skills. Experience with molecular modelling, chemoinformatics tools, databases, statistical analysis and web interfaces. Recommended expertise: Synthetic chemistry, structural biology, biophysical methods and analysis of screening data would be an advantage. Experience with network analysis or machine learning methods would also be highly valued. An interest in computer-‐aided drug design and strong interpersonal skills are essential to establish fruitful collaborations within the consortium.
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