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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Докторантска мрежа2016–2020

FRAGNET · FRAGments training NETwork

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2016-03-01 → 2020-02-29
Финансиране от ЕС
3 842 482 €
Участници
13
Схема
MSCA-ITN-ETN

Линиите свързват координатора с партньорите.

Накратко на български

Методът за откриване на лекарства чрез фрагменти изследва как малки молекули се свързват с протеини в организма. Това помага за създаването на нови фармакологични инструменти и вещества, които служат като основа за разработването на лекарства.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

FRAGments training NETwork

The Marie Skłodowska-Curie Innovative Training Network (ITN) FRAGNET was set up to train fifteen Early Stage Researchers (ESRs) in Fragment-Based Lead Discovery (FBLD, also known as Fragment-Based Drug Design FBDD). FBLD projects begin with screening low molecular weight compounds (so-called fragments) against a biological target (most often a protein). This requires a fragment library and an experimental method to detect binding. Once hits have been identified, the structure of hit fragments binding to the target is determined by X-ray crystallography, NMR methods or molecular modelling approaches. The fragments are then evolved to compounds with higher affinity and activity by structure-based design and synthetic chemistry. The resulting ligands can be used as pharmacological tools and starting points for drug discovery. Europe has played a prominent role in establishing FBLD and the FRAGNET consortium was committed to train 15 multidisciplinary FBLD scientists, as well as to develop the tools and technologies that allow industry and academia to remain competitive in this highly innovative and important research field.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The promise of more efficient lead discovery is fuelling the enthusiasm for fragment-based lead discovery (FBLD). In this approach, highly sensitive biochemical and biophysical screening technologies are being used to detect the low affinity binding of low molecular weight compounds (the so-called fragments) to protein targets that are involved in pathophysiological processes. By investigating the molecular interactions between fragment hit(s) and the target protein, a detailed understanding of the binding event is obtained. This enables the rational and efficient optimisation of the hit fragment. The optimised compounds represent high quality leads for drug development. The necessary FBLD technologies and approaches have emerged mainly from small and specialised biotech companies. At present, FBLD is being adopted throughout pharmaceutical sciences, including by pharmaceutical companies, SMEs and academic research groups. So far, the necessary training that is needed to obtain an holistic view of the possibilities and opportunities that FBLD provides is missing, most likely because the highly multidisciplinary nature of the FBLD work is difficult to capture within one (academic) institute. Therefore, we have established FragNet as a dedicated FBLD training network. The consortium consists of the most prominent pharmaceutical companies, biotech companies and academic groups that have jointly shaped the FBLD research area. FragNet is committed to train 15 ESRs in all facets of FBLD using the combined technologies, skills and knowledge. This will include both research (e.g., technologies on the interface of chemistry and biology) and transferable skill sets (e.g., writing, media training, entrepreneurship and thorough understanding of scientific knowledge transfer). This will enable the ESRs to excel in today’s drug discovery and chemical biology programmes that are performed in public and private organisations in the pharmaceutical sciences.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз