Riniker lab @ ETHZ
@rinikerlab.bsky.social
Riniker research group, ETH Zurich
Our most recent publication with our collaborators at Novartis Biomedical Research uses the idea of Cooperative Free Energy in order to understand the thermodynamics of cooperativity in ternary complexes.
pubs.acs.org/doi/10.1021/...
pubs.acs.org/doi/10.1021/...
Cooperative Free Energy: Induced Protein–Protein Interactions and Cooperative Solvation in Ternary Complexes
Protein–protein interactions (PPIs) play an essential role in biological processes. Molecules that stabilize or induce PPIs in ternary complexes have received growing attention for their therapeutic p...
pubs.acs.org
August 22, 2025 at 6:01 AM
Our most recent publication with our collaborators at Novartis Biomedical Research uses the idea of Cooperative Free Energy in order to understand the thermodynamics of cooperativity in ternary complexes.
pubs.acs.org/doi/10.1021/...
pubs.acs.org/doi/10.1021/...
Our newest publication explores using QM/MM together with RE-EDS to do efficient free-energy calculations, enabling increased accuracy and allowing highly polarized systems to be studied.
#chemsky #compchem
doi.org/10.1021/acs....
#chemsky #compchem
doi.org/10.1021/acs....
Efficient Multistate Free-Energy Calculations with QM/MM Accuracy Using Replica-Exchange Enveloping Distribution Sampling
Calculating free-energy differences using molecular dynamics (MD) simulations is an important task in computational chemistry. In practice, the accuracy of the results is limited by model approximatio...
doi.org
June 15, 2025 at 3:58 AM
Our newest publication explores using QM/MM together with RE-EDS to do efficient free-energy calculations, enabling increased accuracy and allowing highly polarized systems to be studied.
#chemsky #compchem
doi.org/10.1021/acs....
#chemsky #compchem
doi.org/10.1021/acs....
Our new paper in JACS presents the application of a new neural network potential, based on our anisotropic message passing approach, to do QM/MM MD simulations. We achieve chemical accuracy on a few quite different types of chemistry.
doi.org/10.1021/jacs...
#chemsky #compchem #opensource
doi.org/10.1021/jacs...
#chemsky #compchem #opensource
Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
We present the design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechan...
doi.org
February 18, 2025 at 8:12 AM
Our new paper in JACS presents the application of a new neural network potential, based on our anisotropic message passing approach, to do QM/MM MD simulations. We achieve chemical accuracy on a few quite different types of chemistry.
doi.org/10.1021/jacs...
#chemsky #compchem #opensource
doi.org/10.1021/jacs...
#chemsky #compchem #opensource
Our newest preprint describes lwreg, a lightweight system for chemical registration. lwreg has a Python API and makes it easy to store the compound structures you use in your work and the experimental data you generate about them.
chemrxiv.org/engage/chemr...
chemrxiv.org/engage/chemr...
lwreg: A Lightweight System for Chemical Registration and Data Storage
Here, we present lwreg, a lightweight, yet flexible chemical registration system supporting the capture of both two-dimensional molecular structures (topologies) and three-dimensional conformers. lwre...
chemrxiv.org
July 4, 2024 at 3:26 AM
Our newest preprint describes lwreg, a lightweight system for chemical registration. lwreg has a Python API and makes it easy to store the compound structures you use in your work and the experimental data you generate about them.
chemrxiv.org/engage/chemr...
chemrxiv.org/engage/chemr...
Our paper introducing an implicit solvation model for organic molecule in water based on a graph neural network has just appeared:
pubs.rsc.org/en/content/a...
#chemsky
pubs.rsc.org/en/content/a...
#chemsky
June 19, 2024 at 10:58 AM
Our paper introducing an implicit solvation model for organic molecule in water based on a graph neural network has just appeared:
pubs.rsc.org/en/content/a...
#chemsky
pubs.rsc.org/en/content/a...
#chemsky
The most recent paper from our ongoing collaboration with the Zenobi group looks at the impact of desolvation on the stability of beta-hairpin structures.
#chemsky
pubs.acs.org/doi/10.1021/...
#chemsky
pubs.acs.org/doi/10.1021/...
Probing the Stability of a β-Hairpin Scaffold after Desolvation
Probing the structural characteristics of biomolecular ions in the gas phase following native mass spectrometry (nMS) is of great interest, because noncovalent interactions, and thus native fold featu...
pubs.acs.org
May 14, 2024 at 7:29 AM
The most recent paper from our ongoing collaboration with the Zenobi group looks at the impact of desolvation on the stability of beta-hairpin structures.
#chemsky
pubs.acs.org/doi/10.1021/...
#chemsky
pubs.acs.org/doi/10.1021/...
Our newest preprint, introducing a new type of implicit solvation model based on a graph neural network, is now up: chemrxiv.org/engage/chemr...
General Graph Neural Network Based Implicit Solvation Model for Organic Molecules in Water
The dynamical behavior of small molecules in their environment can be studied with molecular dynamics (MD) simulations to gain deeper insight on an atomic level and thus complement and rationalize the...
chemrxiv.org
April 9, 2024 at 4:18 AM
Our newest preprint, introducing a new type of implicit solvation model based on a graph neural network, is now up: chemrxiv.org/engage/chemr...
In our newest paper we look at using five different computational methods applied to the results of MD simulations and NMR relaxation experiments in order to better understand protein motions.
doi.org/10.1063/5.01...
doi.org/10.1063/5.01...
Unraveling motion in proteins by combining NMR relaxometry and molecular dynamics simulations: A case study on ubiquitin
Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to millisecond timescales. As these
doi.org
March 13, 2024 at 5:12 AM
In our newest paper we look at using five different computational methods applied to the results of MD simulations and NMR relaxation experiments in order to better understand protein motions.
doi.org/10.1063/5.01...
doi.org/10.1063/5.01...
Our most recent paper just appeared in JCIM. The title of this one pretty much tells the story: when you assemble a data set by combining data from different literature assays, there is a very good chance that the resulting data contains a lot of noise.
pubs.acs.org/doi/10.1021/...
pubs.acs.org/doi/10.1021/...
Combining IC50 or Ki Values from Different Sources Is a Source of Significant Noise
As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for research...
pubs.acs.org
February 24, 2024 at 2:36 PM
Our most recent paper just appeared in JCIM. The title of this one pretty much tells the story: when you assemble a data set by combining data from different literature assays, there is a very good chance that the resulting data contains a lot of noise.
pubs.acs.org/doi/10.1021/...
pubs.acs.org/doi/10.1021/...
We have a new preprint out which looks at the amount of noise introduced into a data set when we combine data from different ChEMBL assays.
doi.org/10.26434/che...
doi.org/10.26434/che...
Combining IC50 or Ki Values From Different Sources is a Source of Significant Noise
As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for research...
doi.org
January 13, 2024 at 6:33 AM
We have a new preprint out which looks at the amount of noise introduced into a data set when we combine data from different ChEMBL assays.
doi.org/10.26434/che...
doi.org/10.26434/che...
Our paper introducing SIMPD is now out. SIMPD is an algorithm for creating training/test sets for molecular #machinelearning based on an analysis of a large number of real-world medchem projects.
link.springer.com/article/10.1...
#opensource code and data are in github.
github.com/rinikerlab/m...
link.springer.com/article/10.1...
#opensource code and data are in github.
github.com/rinikerlab/m...
SIMPD: an algorithm for generating simulated time splits for validating machine learning approaches ...
Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly...
link.springer.com
December 11, 2023 at 8:19 PM
Our paper introducing SIMPD is now out. SIMPD is an algorithm for creating training/test sets for molecular #machinelearning based on an analysis of a large number of real-world medchem projects.
link.springer.com/article/10.1...
#opensource code and data are in github.
github.com/rinikerlab/m...
link.springer.com/article/10.1...
#opensource code and data are in github.
github.com/rinikerlab/m...
Our most recent preprint describes research done together with the Ferrage group at the Sorbonne in Paris to apply #MolecularDynamics and #NMR to understand protein motions in solution.
chemrxiv.org/engage/chemr...
chemrxiv.org/engage/chemr...
Unraveling Motion in Proteins by Combining NMR Relaxometry and Molecular Dynamics Simulations: A Cas...
Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to nanosecond timescales. As these methods cannot provide an atomically res...
chemrxiv.org
November 27, 2023 at 5:13 AM
Our most recent preprint describes research done together with the Ferrage group at the Sorbonne in Paris to apply #MolecularDynamics and #NMR to understand protein motions in solution.
chemrxiv.org/engage/chemr...
chemrxiv.org/engage/chemr...
Our most recent publication describes a hybrid classical/machine-learning forcefield we've developed for condensed-phase systems.
As usual, it's #opensource, #opendata, and #openaccess
pubs.rsc.org/en/content/a...
As usual, it's #opensource, #opendata, and #openaccess
pubs.rsc.org/en/content/a...
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-p...
Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come wi...
pubs.rsc.org
November 1, 2023 at 11:17 AM
Our most recent publication describes a hybrid classical/machine-learning forcefield we've developed for condensed-phase systems.
As usual, it's #opensource, #opendata, and #openaccess
pubs.rsc.org/en/content/a...
As usual, it's #opensource, #opendata, and #openaccess
pubs.rsc.org/en/content/a...
Our paper introducing DASH, an efficient approach for assigning partial charges to atoms in molecules is now out. The method uses a hierarchy created from attention values from a GNN trained on QM data.
It's #opensource, #opendata, and #openaccess
pubs.acs.org/doi/10.1021/...
It's #opensource, #opendata, and #openaccess
pubs.acs.org/doi/10.1021/...
DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted f...
pubs.acs.org
October 27, 2023 at 8:43 AM
Our paper introducing DASH, an efficient approach for assigning partial charges to atoms in molecules is now out. The method uses a hierarchy created from attention values from a GNN trained on QM data.
It's #opensource, #opendata, and #openaccess
pubs.acs.org/doi/10.1021/...
It's #opensource, #opendata, and #openaccess
pubs.acs.org/doi/10.1021/...