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There is a standard definition of a rotatable bond in chemistry
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REFUTED
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Chemical literature indicates that while common heuristics exist for identifying rotatable bonds, different software packages and studies utilize varying loose or strict definitions, meaning there is no single universal standard.

Evidence against · 2
2024 · cited by 10
Molecular flexibility is a commonly used, but not easily quantified term. It is at the core of understanding composition and size of a conformational ensemble and contributes to many molecular properties. For many computational workflows, it is necessary to reduce a conformational ensemble to meaningful representatives, however defining them and guaranteeing the ensemble's completeness is difficult. We introduce the concepts of torsion angular bin strings (TABS) as a discrete vector representation of a conformer's dihedral angles and the number of possible TABS (nTABS) as an estimation for the ensemble size of a molecule, respectively. Here, we show that nTABS corresponds to an upper limit for the size of the conformational space of small molecules and compare the classification of conformer ensembles by TABS with classifications by RMSD. Overcoming known drawbacks like the molecular size dependency and threshold picking of the RMSD measure, TABS is shown to meaningfully discretize the conformational space and hence allows e.g. for fast checks of the coverage of the conformational space. The current proof-of-concept implementation is based on the ETKDGv3 conformer generator as implemented in the RDKit and known torsion preferences extracted from small-molecule crystallographic data. Following this logic, the size of the conformational space of a molecule, and thus its molecular flexibility, can be estimated by generating a large ensemble of conformers and then pruning them based on a chosen distance metric. One of the most frequently used descriptors to quantify molecular flexibility is the number of rotatable bonds. Though common, this descriptor suffers from a number of drawbacks. Perhaps the largest of these is that it requires a clear, and ideally easily computed, definition of which bonds are rotatable. There are many of such definitions, e.g., the one proposed by Bath et al., 10 but not one used by all. Furthermore, the number of rotatable bonds, being constrained to integer values, provides a very coarse-grained view of the size of the conformational space and ignores the fact that different types of bonds have different degrees of rotational freedom. To overcome these challenges, Kier developed the ϕ index, 3 which provides a continuous description of the flexibility space derived solely based upon information from the molecular graph. A TABS itself is a vector representation for a conformer reduced to a description of its dihedral angles: Each vector element corresponds to the binned value of the torsion about one rotatable bond in the molecule. The TABS representation discretizes the torsion space and is a form of dimensionality reduction that simplifies the analysis and understanding of conformational ensembles. After this definition, it is also clear that TABS and a torsion fingerprint (TF) 6 are inherently different as the TFs operate on a continuous space, as well as treating ring contributions as average sums. TFD 6 itself and TABS are only comparable if a distance metric between two TABS was defined, which has not been done as part of this initial method development. Theory Common Flexibility Metrics Before describing our new flexibility metric nTABS, we provide a short overview over two of the most commonly used flexibility metrics: number of rotatable bonds and Kier ϕ index. 3 Rotatable-Bond Count The most common definition of a rotatable bond is a single bond that is not part of a ring connecting two atoms, which each have at least one other nonterminal substituent. 14 Refinements typically include aspects like ignoring bonds where one atom has only symmetry-equivalent substituents or including bonds in macrocycles. Here, we use the default rotatable bond definition in the RDKit, 15 described in detail in the Supporting Information S1 . Kier ϕ Index As with the rotatable-bond count, Kier treats flexibility as a structural attribute that can be derived directly from the molecular graph. 3 Kier’s reference point for a perfectly flexible molecule is the infinite chain of carbon atoms with sp 3 hybridization (Csp 3 ), which marks the point where the flexibility index ϕ is defined to be infinite. Figure 1 Example of TABS assignment for two example conformers of a molecule. Regular Torsions In a molecule, each rotatable bond, as identified by a chosen definition, can be associated with a distinct torsion profile. These torsion profiles are influenced by the molecule’s overall structure, giving each dihedral an individual profile. Though each torsion is, in principle, unique, it is possible to assign them to a comparatively small number of archetypes, as for instance introduced by Schärfer et al. 18 and Guba et al. While a small overestimation of the number of possible conformers in chains is accepted, this is not defensible when considering ring structures. A quick estimation of the overcount shows the importance of taking correlation into account for rings: Given the SMARTS pattern matching the dihedrals in an aliphatic six-membered ring, there would be three bins per bond, which leads to 3 6 = 729 possible combinations of the six bits of the TABS for the ring. For the purposes of the analysis in this study, we used nTABS to further decompose the Platinum set into three subsets: low flexibility molecules (nTABS < 500), medium flexibility molecules (500 < = nTABS < 10,000), and high flexibility molecules (nTABS > = 10,000). Note on Calculating TABS with ETKDG In order to generate a TABS for a As this library does not cover all bonds assigned to be rotatable using the RDKit’s definition, 15 we need to calculate multiplicities and bins also for these additional rotatable bonds. The additional dihedrals were assigned a multiplicity of six as a default option, binning the torsion profile arbitrarily at 30, 90, 150, 210, 270 and 330°. Analysis For the comparison of the categorization of conformers with TABS and with heavy-atom RMSD, we calculated confusion matrices.
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rails:sufficiency:refuted:for=0+0p:against=2+0p | v55:sufficiency

More against · 1
2026 · cited by 1
Physics-based methods, such as protein-ligand binding free energy calculations, are increasingly used in early-stage drug discovery to prioritize compounds. Accurate free energy estimates require adequate sampling of all relevant protein-ligand conformations, including ligand and protein side chain's rotatable bonds. Sampling issues can arise from slow torsion conformation changes and may manifest as high statistical errors or variability between repeated calculations. However, apparent convergence does not guarantee sufficient sampling, and identifying the underlying causes of slow convergence can be difficult. Here, instead of simply focusing on convergence of free energy estimates, we assess the sampling of specific structural degrees of freedom to identify potential sampling problems. Particularly, we develop an automated method for diagnosing sampling issues caused by slow torsional rotation events in the protein or ligand during binding free energy calculations. Here, our focus is on postsimulation analysis. Our method analyzes torsions in the ligand and in residues near the protein's binding site to define the dihedral angle states for each torsion. We then flag potential sampling issues when there are low transitions in and out of each dihedral angle state. We find that our method automatically detects sampling issues caused by slow torsional rotations that otherwise would have gone unnoticed and may have noticeable impacts on the calculated free energy values. PMC Copyright notice PMCID: PMC13210266  NIHMSID: NIHMS2167487  PMID: 42084183 The publisher's version of this article is available at J Chem Inf Model Abstract Physics-based methods, such as protein-ligand binding free energy calculations, are increasingly used in early-stage drug discovery to prioritize compounds. Accurate free energy estimates require adequate sampling of all relevant protein-ligand conformations, including ligand and protein side chain’s rotatable bonds. Sampling issues can arise from slow torsion conformation changes and may manifest as high statistical errors or variability between repeated calculations. When starting a simulation, uncertainty concerning the orientation of a particular rotatable In trajectory (d), we imagine the worst case scenario where the we only sample the minor torsion state as a result of a slow torsion transition timescale in this system. In real simulations, slow events like those in the hypothetical example of Figure 1 can pose sampling problems that affect our free energy estimates. Such slow motions can include protein side chain rearrangements, motion of ligand internal rotatable bonds, and binding site water rearrangements. Referencing our previous example ( Figure 1 ), in trajectories (b) and (c) the transition between the major to minor torsion orientations are examples of a slow or rare event. We further demonstrate the utility of this tool through case studies where it successfully detects sampling problems in real protein–ligand simulations, often problems which have a serious adverse impact on binding free energy estimates. 3. Methods We seek to develop a method to automatically determine the discrete torsion states each rotatable bond visits in a given simulation. Once these states are determined, we count the number of times each torsion transitions from one state to another. We then use transition counts to assess our level of confidence that a state, or a particular torsion, has been adequately sampled. We first identify torsions of interest For small molecule analysis, we identify torsions of interest as rotatable bonds that fit the following definition: single bonds that are not part of a ring system, are not terminal methyls, and do not contain a triple-bonded atom, using the following default SMARTS pattern: “[!$(*#*)&!D1,$([C;D2]-[O,N,S;H1])]-!@[!$(*#*)&!D1,$([C;D2]-[O,N,S;H1])]” . 37 This definition includes bonds such as the carbon–oxygen bond in an alcohol group or the carbonnitrogen bond in an amine, allowing analysis of torsional motions that may influence hydrogenbond formation. However, users may modify the SMARTS pattern to include additional rotatable bonds, such as those involving terminal hydrogen atoms (i.e. methyls), depending on the requirements of their analysis. This approach corresponds to the looser definition of rotatable bonds, which considers most single, non-ring bonds between non-terminal heavy atoms as rotatable. In contrast, stricter definitions, such as those used by OpenEye. 38 or RDKit’s 37 default strict mode, exclude terminal rotatable bonds like amide C–N (resulting in a dihedral of R-C-N-H) or alcohol C–O bonds (resulting in a dihedral of R-C-O-H). For example, for the molecule shown in Figure 5 , rotation about the connecting single bond results in a transition to an alternate state which is equivalent based on exchange of the phenyl ring’s atoms. This occurs based on a 180 degree flip of the phenyl ring. However, the two rings prefer not to lie in the same plane, and instead tip slightly out-of-plane by a few degrees. If the two rings were in the same plane, there would thus be two peaks separate by 180 degrees, but the out-of-plane tilt (due to sterics) splits each of these two peaks into two smaller peaks, each with a symmetry-equivalent partner separated from it by 180 degrees. Journal of Chemical Theory and Computation 2023, 19, 4863–4882. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (29). Zhang C; Osato M; Mobley DL Kinetics-Based State Definitions for Discrete Binding Conformations of T4 L99A in MD via Markov State Modeling. Journal of Chemical Information and Modeling 2024, 64, 8870–8879. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] (30). Chekmarev DS; Ishida T; Levy RM Long-Time Conformational Transitions of Alanine Dipeptide in Aqueous Solution: Continuous and Discrete-State Kinetic Models. The Journal of Physical Chemistry B 2004, 108, 19487–19495. [ Google Scholar ] (31).
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  1. An Automated Workflow for Diagnosing Sampling Issues Caused by Slow Torsional Motions in Molecular Simulations.peer-reviewedno side taken
  2. Understanding and Quantifying Molecular Flexibility: Torsion Angular Bin Strings.peer-reviewedno side taken
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