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Paper · 2207.09250 · 2022

Roughness of molecular property landscapes and its impact on modellability

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 8 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
coleygroup/rogi canonical 5 of 8
FunctionStatusWhere it lives
MODI Ran coleygroup/rogi/src/rogi/modi.py
code served (permissive licence) · get_code("db40c014a849bfba")
RMODI Ran coleygroup/rogi/src/rogi/modi.py
code served (permissive licence) · get_code("84973c24421d9b67")
get_config_from_root Ran coleygroup/rogi/versioneer.py
code served (permissive licence) · get_code("51944f53542b7e19")
nmoment Ran coleygroup/rogi/src/rogi/roughness_index.py
code served (permissive licence) · get_code("2238073efaa41d2e")
unsquareform Ran coleygroup/rogi/src/rogi/roughness_index.py
code served (permissive licence) · get_code("cd381478e539bfdf")
register_vcs_handler Not yet run coleygroup/rogi/src/rogi/_version.py
code served (permissive licence) · get_code("c55cafde08716f01")
run_command Not yet run coleygroup/rogi/src/rogi/_version.py
code served (permissive licence) · get_code("dbb37355631dabcf")
versions_from_parentdir Not yet run coleygroup/rogi/src/rogi/_version.py
code served (permissive licence) · get_code("5032e3b81c0fffb3")

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Abstract

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.

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