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The fragmentation algorithm still uses a top-down approach, starting with an entire molecular graph and removing each bond successively.
2D approaches make use of the adjacency matrix of the molecular graph, and, therefore they are also called topological-based approaches.
The linear fragments are obtained by iterating over all atoms in a molecular graph and performing an exhaustive search up to a predefined depth d.
We used foldMap to sum up the valences of all bonds connected to the atom in question (this list is directly available from the molecular graph) and then search for the lowest valid valence equal to or greater than this value.
This input specification assures that the service will receive and operate upon an entity of the type molecule that has a SMILES descriptor and that this descriptor has a string value which the service can parse, transform into a molecular graph, and for which it can carry out descriptor calculations with a given API, in this case CDK.
It operates on both the molecular graph and the precomputed generative models of the flexible atom‐pairs.
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Das and Trinajstić [15] compared the first GA index and ABC index for chemical trees, molecular graphs, and simple graphs with some restricted conditions.
The molecular representation is done by molecular graphs and an adapted genetic algorithm is used coupled with a multi level resolution.
In recent years, several such kernels have been published that directly operate on molecular graphs and thus avoid an explicit descriptor representation of the molecules.
Fragments are randomly generated by iterative bond deletion in molecular graphs and hence depart from those obtained by well-defined fragmentation schemes [1].
In this presentation we briefly review these methods and then describe a new multi-objective optimization de novo design algorithm that combines evolutionary techniques with graph-theory to directly manipulate molecular graphs and design structurally diverse molecules satisfying one or more objectives.
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