Fragment OptimizationSimple SMILES String Manipulation Strategies to Optimize a Hit or Fragment

Alongside these methods, simpler nonlearned strategies based on direct SMILES manipulation remain useful, particularly for low data targets. Herein is described a method for fragment optimization in which characters of a SMILES string are systematically replaced with alternative SMILES patterns to generate atom-type swaps, ring expansion/contraction, cyclization, ring opening, and fragment grafts.

The method was initially implemented as a KNIME workflow and demonstrated on fragment-to-lead optimization of fragments from several programs. A fully open-source counterpart written in Python utilizing AutoDock Vina is also provided. Together, these tools are used in parallel with alternative open-source and commercial solutions to accelerate fragment-to-lead optimization through iterative <i>in silico</i> design and profiling, and require no AI/ML or proprietary licensing for the open source python version.