The ICTP-East African Institute for Fundamental Research (EAIFR) wishes to invite you to our next webinar dubbed: Going Beyond Atomistic Scales. This seminar will take place on 5th December, 2025 and will be broadcast live on ZOOM at 4:00 PM Kigali TIme. It will also be recorded and later posted on the ICTP-EAIFR YouTube channel, where one can find all previous  ICTP-EAIFR webinar recordings.

Below are the details:

Speaker: Dr. Steffen Wolf, Institute of Physics, University of Freiburg, Germany.

Title: Calculating drug residence times and affinities: going beyond the limits of atomistic MD simulations

When: December 5, 2025 at 4:00 pm (Kigali time).

Register in advance for this meeting by clicking here.

Abstract

While the computation of binding free energies from atomistic MD simulations of target protein-drug complexes is well established, the calculation of drug (un)binding rates that are linked to improved drug efficacy is a comparatively new field. The connection between a drug’s chemical structure and these rates is still not well understood, but is of high interest for establishing design rules for compounds with tailor-made pharmacokinetics.

To compute (un)binding dynamics and the related affinity constants, here we present a combination of dissipation-corrected targeted MD and temperature-boosted Langevin equation simulations. Using simulations of a few nanoseconds individual length, the fully atomistic data is rationalized in the form of only two profiles, which are the free energy ∆G(x) and friction Γ(x). Using these profiles in Langevin equation-based simulations allows access to dynamics on time scales of seconds to hours—several magnitudes beyond the capabilities of atomistic MD approaches. Analyzing the kinetics-defining transition states ∆G_NEQ(x) and Γ(x) for a range of pharmaceutically relevant target-drug complexes such as Hsp90 or GPCRs, we find that the transition barrier height is typically related to the rupture of enthalpic interactions such as salt bridges and hydrogen bonds, while friction corresponds to the solvation of a ligand and the protein binding site. Electrostatic interactions can both accelerate and slow down unbinding, depending on the individual chemical scaffold of a drug.

While the calculation of binding free energies can in principle be carried out only based on the bound and unbound states, calculation of kinetics is highly dependent on the route a drug takes in and out of its binding site. We present several strategies to find these paths and the path-defining collective variables via dimensionality reduction, graph-based, and machine learning methods. Lastly, we demonstrate how chemically small changes between two compounds can already lead to drastically altered unbinding paths, highlighting the challenges in the computation of (un)binding kinetics.

References

1. Wolf, S. et al. Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations. J. Chem. Inf. Model. 59, 5135–5147 (2019).
2. Wolf, S., Lickert, B., Bray, S. & Stock, G. Multisecond ligand dissociation dynamics from atomistic simulations. Nat. Commun. 11, 2918 (2020).
3. Wolf, S., Post, M. & Stock, G. Path separation of dissipation-corrected targeted molecular dynamics simulations of protein–ligand unbinding. J. Chem. Phys. 158, 124106 (2023).
4. Tänzel, V., Jäger, M. & Wolf, S. Learning Protein–Ligand Unbinding Pathways via Single-Parameter Community Detection. J. Chem. Theory Comput. 20, 5058–5067 (2024).
5. Jäger, M. & Wolf, S. More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations. J. Phys. Chem. B 12, 2825 (2025).

Biography:

Privatdozent Dr. Steffen Wolf is a Computational Biophysicist at the Institute of Physics, University of Freiburg (Germany). His research focuses on nonequilibrium molecular dynamics, free energy landscapes, and data-driven modeling of biomolecular processes.

Dr. Wolf earned his PhD in Theoretical Biophysics from Ruhr-University Bochum in 2009, where he investigated the structure and function of transmembrane proteins using molecular simulations. Following postdoctoral work at the CAS/Max Planck Partner Institute for Computational Biology in Shanghai and the Ruhr-University Bochum, he joined the University of Freiburg in 2016, where he completed his Habilitation in Physics on the nonequilibrium dynamics of living soft matter on atomic scales.

His research combines molecular dynamics, Langevin simulations, and machine learning methods to explore biomolecular kinetics far beyond traditional MD timescales. He has led several national and international research projects funded by the DFG, DAAD, and CECAM, and collaborates extensively with both academic and industrial partners, including Merck, Sanofi, and Givaudan.

Dr. Wolf’s expertise spans non-equilibrium statistical mechanics, dimensionality reduction, kinetic modeling, and quantitative structure–activity relationships, with applications in drug discovery, protein dynamics, and computational chemistry.