Torsion angular bin strings: algorithmic update and additional validation.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Molecular Modeling

Background:

• Torsion Angular Bin Strings (TABS) provide a discrete vector representation for molecular conformers.
• The TABS method allows for counting conformational states and estimating ensemble size (nTABS).
• TABS offers a threshold-independent method for grouping conformers, unlike RMSD or shape Tanimoto.

Purpose of the Study:

• To describe updates to the nTABS algorithm within the TABS package.
• To present a comparative classification study of conformer ensembles using TABS versus shape Tanimoto.
• To validate TABS's capability in meaningfully encoding molecular conformer shapes.

Main Methods:

• The updated nTABS algorithm now utilizes group theory, specifically Burnside's Lemma, to handle molecular topological symmetry.
• This new approach simplifies the algorithm and improves computational efficiency.
• Conformer ensembles were classified using TABS and compared against classifications derived from shape Tanimoto.

Main Results:

• The updated TABS algorithm demonstrates improved computational speed and reduced code complexity.
• Classification of conformer ensembles by TABS shows meaningful correlation with shape Tanimoto.
• Results support the hypothesis that TABS effectively capture the conformational shape information.

Conclusions:

• The revised TABS algorithm offers a more efficient and robust method for analyzing molecular conformational ensembles.
• TABS provides a valuable alternative for classifying molecular shapes, complementing existing metrics.
• This work reinforces the utility of TABS in computational chemistry and drug discovery.