ZIBAffinity: Strong pain relief without addictive drugs

Many people suffer from significant pain. Mainly opioids are used for the treatment of severe pain. However, these opioid drugs (like morphine or fentanyl) lead to side effects: addiction, constipation, sedation, respiratory depression etc. Due to the availability of opioids in the USA, many people became addicted in the last years resulting in the so-called “opioid crisis” that has already severe effects on social life. Molecular Simulation and Computational Drug Design helped to rationally design new opioids which proved to have none of the mentioned side effects in preclinical trials. ZIBAffinity is able to identify potential pain relief drugs by molecular simulation. These drugs then only act in inflamed tissue and not in the brain and, thus, avoid addiction.

Go to our youtube channel for a demo of computational molecular design.

ZIBAffinity: Helping to remove risky molecules from our drinking water  

In industrialized countries the number of different trace pollutants in the water cycle increases year by year. For some of these pollutants we do not know their effects on human body or on the endocrine/hormone system. The decreasing fertility or certain forms of cancer may be caused by these substances. In order to check potential interactions of small molecules with human hormone receptors (like the estrogen receptor), ZIBAffinity uses molecular simulation to predict potential risks stemming from trace pollutants and their transformation products.

ZIB – The Zuse Institute Berlin is located in Dahlem, Berlin. Besides others, it carries out research on molecular and biological processes, read more here or contact Dr. Marcus Weber (contact details available here).

The details …

ZIBaffinity was designed to provide high-quality binding affinities ΔG of small chemical compounds to biological target molecules using atomistic molecular dynamics simulations on the basis of the Amber force field and methods of statistical thermodynamics.

Model Formulation

ΔG is calculated as the free energy difference between two distinct states of the ligand molecule, bound to the target and, respectively, free in water. Having uploaded a small drug-like molecule under observation as input, the user needs to select one or more protein target structures from a database of force field-parameterized models resulting in one job per target-ligand combination. Using modern cloud-computing technologies, the job is submitted to and processed on a high performance computer. The main results are then supplied to the user.


Ensuing from the uploaded small molecule, numerous MD simulations including different starting positions of the molecular complex (distributed according to the icosahedron’s symmetry) and one unbound simulation are carried out simultaneously. The optimal binding mode (complex conformation) is extracted from that data and provided as a 3D molecular structure serving, along with thermo-statistical data as the basis for absolute or relative binding affinity estimation.

The affinity is estimated as a linear combination of averages of molecular observables according to an extended linear interaction energy model

where the parameter coefficients xi need to be determined empirically in advance. Thus, the target data base only provides protein, for which a training set of ligands with known binding affinities were available for the purpose of training these weights. Currently, only one target structure is provided, the alpha estrogen receptor, for which a highly reliable linear model has been developed.

Further force field parameterized targets that are critical for human health will be added to the data base in the next future.

Simulation Technology
    • Molecules parameterized according to Amber force field (Amber99sb & GAFF)

    • Estimation of ligand charges with the AM1BCC method

    • Explicit solvation through TIP4Pew water model

    • Deterministic molecular dynamics simulations of ligand molecule (bound to target and free in solution) using Gromacs

    • Calculation of absolute binding free energies ΔG for biological host–guest systems

    • Calculation of unweighted thermodynamic contributions to ΔG in case of target molecules lacking training set

    • Determination of a favourable host–guest binding mode

    • Atomic Cartesian coordinates of a small chemical compound in MOL2 or PDB file format uploaded by user

    • One or more target molecules selected by user from a data base of parameterized proteins

    • Atomic coordinates of favourable protein-ligand binding mode output in PDB format

    • Either free energy of binding or, if no weights for linear model exist, the single energy terms


V. Durmaz, S. Schmidt, P. Sabri, C. Piechotta, M. Weber: A hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens. Journal of Chemical Information and Modeling, 53:2681-2688, 2013.

V. Spahn, G. Del Vecchio, D. Labuz, A. Rodriguez-Gaztelumendi, N. Massaly, J. Temp, V. Durmaz, P. Sabri, M. Reidelbach, H. Machelska, M. Weber, C. Stein: A non-toxic pain killer designed by modeling of pathological receptor conformations. Science, 355:966-969, 2017.

K. Heye, D. Becker, C. Lütke-Eversloh, V. Durmaz, T. A. Ternes, M. Oetken, J. Oehlmann: Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test. Water Research, 98:19-72, 2016.