Enumerating common molecular substructures

Engler, Martin S.; El-Kebir, Mohammed; Mulder, Jelmer; Mark, Alan E.; Geerke, Daan P.; Klau, Gunnar W.

doi:10.7287/peerj.preprints.3250

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…It can be shown that the k-MCF-E problem is a generalisation of enumerating all maximal CCIS (MCCIS-E). 132 Note that there is a fundamental difference between enumerating all maximal CCIS (MCCIS-E) and the well-known problem of finding the maximum (largest) CCIS, for which many exact algorithms and heuristics have been proposed, especially in the context of molecular graphs. [133][134][135] For this application, we adapted the MCCIS-E algorithm of Koch 136 to k-MCF-E.…”

Section: Molecules and Graph Matchingmentioning

confidence: 99%

“…[133][134][135] For this application, we adapted the MCCIS-E algorithm of Koch 136 to k-MCF-E. When combined with additional data reduction techniques, we showed that our algorithm for k-MCF-E for k > 0 is highly efficient even for large molecular graphs, 132 allowing databases containing 100,000s of drug-like molecules to be routinely screened.…”

Section: Molecules and Graph Matchingmentioning

confidence: 99%

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Improving automated force-field parametrisation for molecular simulation: a graph approach

Caron¹

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Classical molecular dynamics (MD) techniques offer atomic-level insights into a wide range of physical systems, including biomolecular systems. They rely on empirical, parametrised equations (force fields) to describe the interaction potential between the constituents of the systems. Their predictive ability is critically dependent on the accuracy of these parameters. Highly optimised, well-validated parameters have been developed for many important biomolecules such as proteins, lipids and sugars. However, developing parameters for small molecules such as drugs is very challenging given the scale of chemical space. For instance, the ChEMBL database contains in excess of 1.7 million bioactive compounds. Although stand-alone software (e.g antechamber 1, 2 ) or web-servers (e.g. the Automated Topology Builder, 3-5 atb.uq.edu.au) have been developed to assign parameters to drug-like molecules, they usually rely on fitting to quantum-mechanical (QM) calculations in combination with sets of empirical rules, and their applicability is limited to small molecules (less than a few tens of atoms). Automated parametrisation of large, bio-active, and often biologically relevant molecules is still impractical and greatly limits the predictive power of simulations involving such compounds.This thesis focuses on the use of graph-based approaches to develop new automated parametrisation paradigms which can exploit large data sets to simultaneously develop and assign force field parameters.An efficient Linear Programming method for deducing the charge state and bond order, based only on the molecular connectivity and chemical elements of its atoms, was developed. The algorithm was validated against the MMFF94 dataset containing 761 molecules with manually assigned bond orders. The approach was extended to i) cap molecular fragments (molecules having some atoms with incomplete valences) and ii) enumerate tautomeric (protomerism) forms of molecules. These extensions can be used for solving various problems related to drug-design and fragment-based empirical force-field parametrisation and can be applied to molecules containing hundreds of atoms.A new method for predicting chemically equivalent atoms in a molecule was developed. This method relies on identifying automorphisms of the molecule. False positives (non-chemically equivalent atoms treated as equivalent by the previous approach) were addressed by implementing a series of exceptions for double bonds, non-invertable rings and stereotopic atoms, respectively. This method was used to symmetrise force-field terms between chemically equivalent atoms in the Automated Topology Builder (ATB, 3-5 atb.uq.edu.au).Building on these chemical equivalence relationships, a representation-independent, symmetrycorrected distance metric was developed (Blind RMSD). This facilitates the alignment of molecules with identical chemical graphs but different atom naming and indexing arbitrarily assigned. This approach was extended to fragments (i.e. subset of atoms in a molecules) for mapping graph n...

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Section: Molecules and Graph Matchingmentioning

confidence: 99%

Section: Molecules and Graph Matchingmentioning

confidence: 99%

Improving automated force-field parametrisation for molecular simulation: a graph approach

Caron¹

View full text Add to dashboard Cite

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Enumerating common molecular substructures

Cited by 1 publication

References 11 publications

Improving automated force-field parametrisation for molecular simulation: a graph approach

Improving automated force-field parametrisation for molecular simulation: a graph approach

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