ArticleACS medicinal chemistry letters2026
Fragment OptimizationSimple SMILES String Manipulation Strategies to Optimize a Hit or Fragment.
Article in ACS medicinal chemistry letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
AI/ML-based tools for generative and predictive modeling have become important contributors to small molecule drug discovery, enabling virtual compound design and ADME profiling prior to synthesis. 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
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