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H2020Докторантска мрежа2021–2025

AIDD · Advanced machine learning for Innovative Drug Discovery

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

Период
2021-01-01 → 2025-03-31
Финансиране от ЕС
3 926 573 €
Участници
17
Схема
MSCA-ITN

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Накратко на български

Изкуственият интелект се използва за проектиране на нови лекарства и предвиждане на техните странични ефекти. Това помага за по-бързото откриване на подходящи молекули и оптимизиране на химичните процеси при създаването им.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Advanced machine learning for Innovative Drug Discovery

AIDD was a Marie Skłodowska-Curie Initial Training European Industrial Doctorate Network at the interface between chemistry, computer science, and life sciences, providing well-structured multidisciplinary training and educating highly-in-demand machine learning specialists capable of operating in interdisciplinary and international research and business settings. Cornerstones of AIDD’s curriculum included online lectures and periodic schools delivered by internationally-leading experts, coming from a balanced consortium of researchers in academia, SMEs, and large companies. The scientific goals of the AIDD project were: 1) To develop a suite of interoperable, open-source modular AI tools to support core tasks in computational chemistry and drug discovery, tailored primarily for the pharmaceutical sector. 2) To optimise de novo drug design using multi-modal data integration, enhancing molecular design through integration of diverse data types. 3) To implement AI-driven toxicity and side-effect prediction pipelines by developing robust AI modules capable of filtering out candidate molecules with undesirable properties such as toxicity, non-specificity, and adverse side effects. 4) To push innovation in reaction prediction and retrosynthetic planning, streamlining synthetic chemistry. 5) To create next-generation tools for Molecular Dynamics (MD) simulations: ML-enhanced tools to accelerate molecular dynamics simulations for binding affinity prediction. 6) To integrate explainability and expert knowledge into ML models, developing methodologies to incorporate human expert reasoning and provide interpretable AI outputs. AIDD has produced a robust and interoperable suite of open-source tools addressing all of these aims, collectively forming the “One Chemistry” framework, which holds strong exploitation potential across the pharmaceutical industry and academic research.

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

Цел на проекта

The dramatic increase in using of Artificial Intelligence (AI) and machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro biotech, and other chemical companies. However, the application of AI in these fields is not straightforward and requires excellent knowledge of chemistry. Thus, there is a strong need to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry, which is the prime goal of the AIDD proposal. Research WPs include sixteen topics selected to cover the key innovative directions in machine learning in chemistry. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated ""One Chemistry"" model that can predict outcomes ranging from different properties to molecule generation and synthesis. Training on various modalities allows the model to understand how to intertwine chemistry and biology to develop a new drug making its design robust and explainable. All partners agreed to make their software open source. It will boost the field and will provide the broadest possible dissemination of the results both to the academy and industry, including SMEs. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools. The IP policy and commercial exploitation of the project results have the highest priority supported by intellectual property asset management organizations. Comprehensive public engagement activities will complement the dissemination of results to the scientific community.""

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз