Meeting organized together with Academy of Sciences of Lithuania

Meeting is jointly held with

5^th Baltic Biophysics Conference (October 8-9^th)

Conference Scope

This meeting brings together experimentalists, data scientists, method developers, and database curators working in molecular biophysics and related fields. The conference covers the full experimental-data lifecycle, from experimental design and raw instrument output, through data processing and modeling, to database deposition and secondary reuse.

The conference aims to address the practical implementation of FAIR principles. Contributions are expected to present and discuss challenges, solutions, and case studies related to data quality, standardization, interoperability, uncertainty treatment, reproducibility, and reuse. Artificial intelligence and machine learning are important users of large, high-quality datasets. However, the primary emphasis should be on building robust, transparent, and reusable biophysical datasets that serve the broader scientific community. By enabling cross-method and cross-disciplinary dialogue, this meeting aims to move the field toward shared standards and consensus guidelines, strengthening the foundations for reliable, data-driven molecular biophysics.

Conference Goals

The 2nd Meeting Towards Data FAIRness in Molecular Biophysics aims to advance the implementation of community practices for FAIR (Findable, Accessible, Interoperable, Reusable) principles in experimental molecular biophysics, with an emphasis on protein-ligand interaction data and related biophysical measurements.

Key objectives include:

• Aligning FAIR principles with biophysical experiments across the data lifecycle, from raw instrument output to reported thermodynamic and kinetic parameters;
• Improving data reliability, transparency, uncertainty treatment, and reproducibility;
• Defining minimal requirements for reporting experimental results, including raw data, metadata, and data provenance;
• Promoting standardized, interoperable data formats for machine-learning applications;
• Advancing data accessibility, deposition, and long-term reuse;
• Fostering community-driven best practices and consensus guidelines for FAIR data reporting.

The conference emphasizes, but is not limited to, the following experimental techniques:

• Isothermal Titration Calorimetry (ITC) – thermodynamic parameters (K_d, ΔG, ΔH, ΔS, ΔC_p) and, where applicable, kinetic information;
• Enzymatic Activity Inhibition Assays – focus on single-site competitive inhibition, including stopped-flow kinetic assays (SFA); main parameters IC₅₀, K_i, K_d;
• Thermal Shift Assay (TSA) / Differential Scanning Fluorimetry (DSF) – binding-related thermodynamic parameters derived from thermal stability measurements (K_d, ΔG);
• Surface Plasmon Resonance (SPR) – kinetic and thermodynamic parameters (k_on, k_off, K_d, ΔG);
• Microscale Thermophoresis (MST) – thermodynamic parameters (K_d, ΔG).
• Separation-based techniques (NECEEM, ACTIS) – kinetic and thermodynamic parameters (k_on, k_off, K_d)