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(odnośnik otworzy się w nowym oknie)) 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.
