Chemistry workshops

This course develops a metabolomics investigation from experimental design through data preparation and statistical interpretation. Lectures, practical work and group discussion connect analytical chemistry and data collection with reproducible analysis. Exercises cover R data handling and visualisation, xcms processing of untargeted mass-spectrometry data, quality control, missing values and compound annotation. The final module compares univariate tests, multiple-testing correction, principal-component methods, partial least squares and random forests for metabolomic matrices. Sessions: 16, 17, 18 and 20 November, 09:30–13:30, Europe/Berlin (CET, UTC+01:00); no session on 19 November. Live online training; fee €480. Familiarity with R is assumed; prior metabolomics experience is useful.

A five-day research workshop combines molecular simulations and AI-assisted drug discovery with practical hacking sessions and direct mentoring. Training covers enhanced sampling, QM/MM simulation and free-energy calculations, with discussion of current computational research workflows. The meeting brings together forty participants spanning master’s students, doctoral researchers, postdoctoral researchers and faculty, alongside invited experts from Europe, the United States and South America. Activities take place at Universidad de los Andes and Universidad Nacional de Colombia in Bogotá. Pre-registration is first come, first served through 20 October; the local organizer lists a COP 95,000 fee including four lunches and separate travel/accommodation scholarship support.

Apply machine learning throughout drug discovery, from biological target identification to molecular screening. The workshop explains model design, training, evaluation and prediction interpretation, then develops deep-learning and graph-neural-network methods in PyTorch. Practical work turns molecular and omics data into model inputs, implements a graph model from a scientific paper, and examines disease classification and target discovery. The final day builds pipelines from research databases and introduces language-model fine-tuning using biological data. Live online, 30 November–3 December, 14:00–19:00 Europe/Berlin (CET). Fee €480. Aimed at MSc/PhD students and early-career scientists, with basic Python, molecular biology, chemistry and data-analysis knowledge. Previous machine-learning experience is unnecessary.

Work through mass-spectrometry proteomics analysis in R and Bioconductor, from raw observations to statistical interpretation. Practical tutorials cover data formats, extraction and visualization, integration of identification results, quantitative data structures and preprocessing. The final session uses statistical analysis to identify proteins with different abundance between experimental groups. 2027-01-27–2027-01-29, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €380. Working R knowledge is required. Mass-spectrometry or proteomics familiarity is useful but not essential; typical datasets and instruments are introduced during the course.

This hands-on course focuses on analysing, interpreting and statistically evaluating quantitative proteomics data. Researchers with basic mass-spectrometry knowledge learn how stable-isotope labelling and label-free approaches support biological experiments, and how to select suitable hardware, software and analysis strategies. Computer-based practicals and group exercises develop skills for handling quantitative data, while lectures connect protein regulation, cellular communication, functional and chemical proteomics, and post-translational modifications. A dedicated session examines practical uses of AI in proteomics and mass spectrometry. The course is relevant both to wet-lab researchers seeking computational experience and to researchers deepening their understanding of quantitative methods.

A graduate school and research workshop examine water from molecular to planetary scales. The programme connects hydrogen bonding and quantum effects with collective dynamics, interfaces, biological function, extreme conditions and atmospheric water. Participants study first-principles simulations, machine-learned potentials and multiscale modelling. The first week provides foundational training; the second features current research, discussions and posters. Applicants should have grounding in statistical mechanics, quantum mechanics and molecular simulation or related computation. Graduate students, postdocs and early-career faculty are the main audience, with limited consideration of senior undergraduates. ICTS provides accommodation for outstation participants.

The relationship between protein structure and function is fundamental to biology. Proteins are inherently dynamic, adopting multiple conformations that enable folding, catalysis, molecular recognition and signal transduction, yet capturing these processes across timescales from femtoseconds to hours remains a major challenge. Time-resolved spectroscopy probes excited states and tracks the kinetic evolution of intermediates generated in response to external stimuli, while time-resolved X-ray methods, including crystallography and solution scattering, provide atomic-resolution snapshots of structural changes and information on conformational dynamics. Because no single technique captures the full complexity of biomolecular dynamics, molecular dynamics simulations and quantum-mechanical calculations are essential for interpreting and integrating experimental findings. This FEBS Workshop brings together researchers in time-resolved spectroscopy, time-resolved X-ray diffraction and scattering, and computational methods to present recent advances, discuss current challenges and strengthen interdisciplinary collaboration. It places particular emphasis on triggering strategies beyond light activation, including ligand mixing, photocage release, microfluidics and chemically triggered reactions, extending time-resolved studies to biological systems that are not intrinsically light-sensitive, and highlights instrumentation and methods developed at large-scale research facilities. Topics: time-resolved spectroscopy; time-resolved serial crystallography at synchrotrons and XFELs; time-resolved X-ray solution scattering; time-resolved cryo-EM; sensory photoreceptors of optogenetic interest, membrane proteins, photosynthetic systems, UV damage, heme proteins and flavoproteins; triggering strategies including chromophore excitation, photo-uncaging, mixing and diffusion, temperature jumps and electric-field stimulation; computational biology (MD simulations, DFT, QM/MM); and software development for time-resolved spectroscopy and serial crystallography. This third edition builds on EMBO Dynamic Photobiology 2023 and FEBS Dynamic Photobiology 2025.

Learn metabolomics experimental design, LC-MS and imaging acquisition, quality control, data processing, compound annotation and modelling through lectures, practicals and public-data case studies. The course uses Galaxy and MetaboLights to teach standardised workflows, open data and submission practices, including lipidomics and biological interpretation. It is intended for researchers new to metabolomics; basic command-line or Galaxy familiarity helps. There are 30 selected places. Fees are £975 for academia and £1,275 for industry, including four nights’ accommodation and programme catering. Applications open 2 November 2026 and close at 23:59 UK time on 31 January 2027; decisions follow by 12 February.

The Society for Mycotoxin Research brings together research on fungal toxins, from their occurrence and management to the fungal biology, ecology and genetics that determine production. The workshop connects analytical methods with toxicology, exposure biomonitoring and approaches to remediation and mitigation. A travel-support scheme for eligible early-career scientists aged 40 or younger from designated developing countries accepts applications from November 1 through December 31, 2026; it requires a scientific contribution and supports in-person attendance.

The programme asks how mathematical advances can improve quantum and computational chemistry as computational power, AI and experimental capabilities evolve. Topics include foundations of established chemical methods, new numerical approaches, quantum algorithms, AI and practical applications. Talks and open discussions connect mathematics, chemistry, physics and computer science; dedicated graduate-student and postdoctoral sessions support training. The workshop seeks combined methods relevant to areas such as clean energy, pharmaceuticals and quantum technologies.

Process mass-spectrometry proteomics data from spectra through peptide identification and quantification to protein lists and functional interpretation. Practical training covers search and post-processing tools, quantitative and data-independent methods, public-data reuse, proteogenomics, PRIDE, ProteomeXchange, UniProt and interaction resources including IntAct and IMEX. Participants need a scientific degree; familiarity with proteomics mass spectrometry is helpful. There are 30 selected places, costing £975 for academia or £1,275 for industry. Applications open 14 December 2026 and close at 23:59 UK time on 14 March 2027, with decisions by 26 March. The confirmed 12–16 July dates replace the previously advertised 5–9 July schedule.