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          <dc:title>Fragment
OptimizationSimple SMILES String
Manipulation Strategies to Optimize a Hit or Fragment</dc:title>
          <dc:creator>Kevin P. Cusack (2365519)</dc:creator>
          <dc:creator>Jake M. Aquilina (12871972)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Molecular Biology</dc:subject>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>Physiology</dc:subject>
          <dc:subject>Pharmacology</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Immunology</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Computational  Biology</dc:subject>
          <dc:subject>low data targets</dc:subject>
          <dc:subject>become important contributors</dc:subject>
          <dc:subject>source counterpart written</dc:subject>
          <dc:subject>alternative smiles patterns</dc:subject>
          <dc:subject>adme profiling prior</dc:subject>
          <dc:subject>smiles string</dc:subject>
          <dc:subject>alternative open</dc:subject>
          <dc:subject>type swaps</dc:subject>
          <dc:subject>systematically replaced</dc:subject>
          <dc:subject>silico &lt;/</dc:subject>
          <dc:subject>several programs</dc:subject>
          <dc:subject>ring opening</dc:subject>
          <dc:subject>ring expansion</dc:subject>
          <dc:subject>proprietary licensing</dc:subject>
          <dc:subject>predictive modeling</dc:subject>
          <dc:subject>lead optimization</dc:subject>
          <dc:subject>knime workflow</dc:subject>
          <dc:subject>initially implemented</dc:subject>
          <dc:subject>generate atom</dc:subject>
          <dc:subject>fully open</dc:subject>
          <dc:subject>commercial solutions</dc:subject>
          <dc:subject>also provided</dc:subject>
          <dc:description>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 &lt;i&gt;in silico&lt;/i&gt; design and profiling,
and require no AI/ML or proprietary licensing for the open source
python version.</dc:description>
          <dc:date>2026-09-22T00:00:00Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1021/acsmedchemlett.6c00181.s002</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Fragment_Optimization_Simple_SMILES_String_Manipulation_Strategies_to_Optimize_a_Hit_or_Fragment/33965701</dc:relation>
          <dc:rights>CC BY-NC 4.0</dc:rights>
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