45 citations · 91 across the 4 of their papers we have counts for
5 papers
Broad chemical transferability in structure-based coarse-graining
Kiran H. Kanekal, Joseph F. Rudzinski, Tristan Bereau
Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-reso…
Resolution limit of data-driven coarse-grained models spanning chemical space
Kiran H. Kanekal, Tristan Bereau
Increasing the efficiency of materials design and discovery remains a significant challenge, especially given the prohibitively large size of chemical compound space. The use of a…
Controlled exploration of chemical space by machine learning of coarse-grained representations
Christian Hoffmann, Roberto Menichetti, Kiran H. Kanekal +1
The size of chemical compound space is too large to be probed exhaustively. This leads high-throughput protocols to drastically subsample and results in sparse and non-uniform data…
Drug-membrane permeability across chemical space
Roberto Menichetti, Kiran H. Kanekal, Tristan Bereau
Unraveling the relation between the chemical structure of small drug-like compounds and their rate of passive permeation across lipid membranes is of fundamental importance for pha…
In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force
Roberto Menichetti, Kiran H. Kanekal, Kurt Kremer +1
The partitioning of small molecules in cell membranes---a key parameter for pharmaceutical applications---typically relies on experimentally-available bulk partitioning coefficient…