143 citations · 150 across the 7 of their papers we have counts for
7 papers
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes
Justin Diamond, Markus Lill
Understanding the macroscopic characteristics of biological complexes demands precision and specificity in statistical ensemble modeling. One of the primary challenges in this doma…
Improving Protein-peptide Interface Predictions in the Low Data Regime
Justin Diamond, Markus Lill
We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture that learns an inter-facial joint distribution of residual contact…
Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation
Soo Jung Lee, Amr H. Mahmoud, Markus A. Lill
The Targeted Free Energy Perturbation (TFEP) method aims to overcome the time-consuming and computer-intensive stratification process of standard methods for estimating the free en…
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks
Ahmadreza Ghanbarpour, Markus A. Lill
De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the…
Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix
Amr H. Mahmoud, Jonas F. Lill, Markus A. Lill
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep…
On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning
Ahmadreza Ghanbarpour, Amr H. Mahmoud, Markus A. Lill
The calculation of thermodynamic properties of biochemical systems typically requires the use of resource-intensive molecular simulation methods. One example thereof is the thermod…