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HEДокторантска мрежа2023–2027

AiChemist · Explainable AI for Molecules - AiChemist

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

Период
2023-09-01 → 2027-08-31
Финансиране от ЕС
3 028 356 €
Участници
29
Схема
HORIZON-TMA-MSCA-DN-ID

Линиите свързват координатора с партньорите.

Накратко на български

Изкуственият интелект за молекули и химични реакции се развива така, че да обяснява логиката зад своите прогнози, например при дизайна на лекарства. Това помага за създаването на по-безопасни химикали и намалява разходите и вредните емисии при експериментите.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Explainable AI for Molecules - AiChemist

Europe’s medicines and chemicals sectors face twin pressures: accelerating discovery while increasing safety, transparency and sustainability. Recent developments in AI promise dramatic gains, yet “black-box” predictions are difficult to trust, reproduce or regulate. AiChemist addresses this issue by making molecular and reaction modelling explainable from the start and by co-designing methods with industrial and regulatory stakeholders so that results are scientifically robust, actionable for chemists, and acceptable to assessors. Today’s models often fail outside their training domain, struggle to generalise across data modalities (small molecules, proteins, reactions), and rarely communicate why a prediction should be believed. At the same time, experimental campaigns (e.g., reaction optimisation) are costly and carbon-intensive. AiChemist (https://aichemist.eu) addresses these gaps with open, benchmarked AI methods that couple representation learning with mechanistic and quantum-aware reasoning, and with a training programme that equips 14 DCs (doctoral candidates) to carry these practices into industry and academia. The main objectives of AiChemist are: 1. Develop and benchmark explainable molecular, reaction and protein representations that improve accuracy, speed and applicability domain versus conventional physics-based/ML baselines. 2. Advance mechanistic and quantum-informed models (e.g., reaction-outcome predictors, QM-derived descriptors) to ground AI decisions in chemical theory. 3. Bridge AI outputs and chemical intuition through practical explainable AI (XAI) workflows for toxicity, drug response and reaction design—including uncertainty, multi-objective trade-offs, and human-interpretable rationales. 4. Validate on public and proprietary datasets, release open, privacy-aware tools. 5. Train DCs through coordinated schools and secondments spanning academia and pharma, with the involvement of regulators in the supervisory board, ensuring durable uptake and technology transfer. By improving trust, portability and efficiency of AI across discovery pipelines, AiChemist aims to reduce experimental iterations and compute budgets; enable safer medicines and chemicals via interpretable toxicity predictions; protect proprietary data while encouraging model exchange; and cultivate a new cohort of researcher-innovators fluent in XAI, open science and responsible research. The expected gains—faster, cheaper and greener design with explanations that chemists and regulators can use—position AiChemist to contribute to Europe’s strategic goals for innovation, safety and sustainability.

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

Цел на проекта

Optimising biological activity and physico-chemical properties, while minimising their toxicity, are objectives when developing new compounds in chemical industries. Advanced machine learning (AI) methods are indispensable to this process. They are also increasingly used in environmental chemistry to identify compounds damaging to the environment and humans. Traditional machine learning (ML) methods provide reliable predictions though only for compounds similar to the training set, thus defining their applicability domain (AD). Emerging representation learning approaches can efficiently approximate the physical interactions of molecules with an accuracy comparable to physics-based methods in only fractions of time. Models based on these representations should have much larger AD due to pre-training on large chemical sets of theoretical values. Here we will develop and benchmark representation learning approaches, addressing their accuracy and ADs, using public and in-house data for endpoints ranging from chemical reactions to toxicity. While explainable AI (XAI) methods are actively developing in the ML community, there is a gap with their use in chemistry, i.e. there is a need to translate their results to the end users, chemists and regulatory bodies. Since the research program is tightly coupled with the target users - large companies, regulatory agencies and SMEs - it provides a clear path for technology transfer from academia to industry. AiChemist will provide structured training to its fellows through a combination of online courses and schools, strengthening European innovation capacity in the education of specialists in AI methods. The fellows will receive comprehensive training in transferable skills. The complementary expertise and strong commitment of the partners make this ambitious innovative research program realistic via the proper allocation of individual tasks and resources, as described below.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз