Bioisosterism is a field that is widely applied to biological molecules, including drugs and agrochemicals. Bioisosterism is the replacement of an active fragment in a molecule with another fragment similar in activity. The replacement is designed to alter the behavior of the molecule in its target environment. In previous work a bioisostere database called the Quantum Isostere Database (QID) was built out of descriptors derived from the theory of Quantum Chemical Topology (QCT). The current work aims to expand the existing QID to include ring fragments. A series of rings were characterised by QCT properties taken from the ring. It was found that four features of a ring each independently have a systematic effect on the ring’s properties. In other words, each of the characteristics of a ring can be changed and have the same effect on the ring’s properties irrespective of the other ring features. The rings were also characterised using the three QCT properties taken from a point within the ring. The three properties established a space where rings were positioned based on their respective three properties. The positions of the rings showed that the space was able to discern between ring types, and that the features of a ring could be predicted if only its three properties were known. To improve the QID the alignment method and scoring were tested. The alignment procedure is unable to correctly align collinear fragments. Therefore, a principal axis alignment procedure was successfully employed to align collinear fragments. For terminal fragments an alternative alignment procedure was proposed to account for the increased rotational freedom. A global axis system meant that the direction dependent properties for all fragments were expressed in this new axis system. This idea was extended further and it was found that the geometry of a molecule was imprinted in the electrostatics when they were expressed in the global axis system. Finally, a pKa prediction method which correlates a single ab initio bond length was tested against two data sets (enols and guanidines). The method relies on subsets to form, where molecules within a subset share a chemical or structural commonality. These subsets were able to distinguish between the five tautomeric forms for the guanidines and different conformations for the enols. All predictions were within 1.0 pKa units of experimental values.

Force field methods are used to investigate the properties of a wide variety of chemical systems on a routine basis. The expression for the electrostatic energy typically does not take into account the anisotropic nature of the atomic electron distribution or the dependence of that distribution on the system geometry. This has been suggested as a cause of the failure of force field methods to reliably predict the behaviour of chemical systems. A method for incorporation of anisotropy and polarisation is described in this work. Anisotropy is modelled by the inclusion of multipole moments centred at atoms whose values are determined by application of the methods of Quantum Chemical Topology. Polarisation, the dependence of the electron distribution on system geometry, is modelled by training machine learning models to predict atomic multipole moments from knowledge of the nuclear positions of a system. The resulting electrostatic method can be implemented for any chemical system. An application to progressively more complex systems is reported, including small organic molecules and larger molecules of biological importance. The accuracy of the method is rigorously assessed by comparison of its predictions to exact interaction energy values. A procedure for generating transferable atomic multipole moment models is defined and tested. The electrostatic method can be combined with the empirical expressions used in force field calculations to describe total system energies by fitting parameters against ab initio conformational energies. Derivatives of the energy are given and the resulting multipolar polarisable force field can be used to perform geometry optimisation calculations. Future applications to conformational searching and problems requiring dynamic descriptions of a system are feasible.

Yes / The optimisation of a peptide-capped glycine using the novel force field FFLUX is presented. FFLUX is a force field based on the machine-learning method kriging and the topological energy partitioning method called Interacting Quantum Atoms. FFLUX has a completely different architecture to that of traditional force fields, avoiding (harmonic) potentials for bonded, valence and torsion angles. In this study, FFLUX performs an optimisation on a glycine molecule and successfully recovers the target density-functional theory energy with an error of 0.89 ± 0.03 kJ mol−1. It also recovers the structure of the global minimum with a root-mean-squared deviation of 0.05 Å (excluding hydrogen atoms). We also show that the geometry of the intra-molecular hydrogen bond in glycine is recovered accurately. / EPSRC Established Career Fellowship [grant number EP/K005472]

Yes / Key to progress in molecular simulation is the development of advanced models that go beyond the limitations of traditional force fields that employ a fixed, point charge‐based description of electrostatics. Taking water as an example system, the FFLUX framework is shown capable of producing models that are flexible, polarizable and have a multipolar description of the electrostatics. The kriging machine‐learning methods used in FFLUX are able to reproduce the intramolecular potential energy surface and multipole moments of a single water molecule with chemical accuracy using as few as 50 training configurations. Molecular dynamics simulations of water clusters (25–216 molecules) using the new FFLUX model reveal that incorporating charge‐quadrupole, dipole–dipole, and quadrupole–charge interactions into the description of the electrostatics results in significant changes to the intermolecular structuring of the water molecules. / EPSRC. Grant Number: K005472

Force fields have been an integral part of computational chemistry for decades, providing invaluable insight and facilitating the better understanding of biomolecular system behaviour. Despite the many benefits of a force field, there continue to be deficiencies as a result of the classical architecture they are based upon. Some deficiencies, such as a point charge electrostatic description instead of a multipole moment description, have been addressed over time, permitted by the ever-increasing computational power available. However, whilst incorporating such significant improvements has improved force field accuracy, many still fail to describe several chemical effects including polarisation, non-covalent interactions and secondary/tertiary structural effects. Furthermore, force fields often fail to provide consistency when compared with other force fields. In other words, no force field is reliably performing more accurately than others, when applied to a variety of related problems. The work presented herein develops a next-generation force field entitled FFLUX, which features a novel architecture very different to any other force field. FFLUX is designed to capture the relationship between geometry and energy through a machine learning method known as kriging. Instead of a series of parameterised potentials, FFLUX uses a collection of atomic energy kriging models to make energy predictions. The energies describing atoms within FFLUX are obtained from the Interacting Quantum Atoms (IQA) energy partitioning approach, which in turn derives the energies from the electron density and nuclear charges of topological atoms described by Quantum Chemical Topology (QCT). IQA energies are shown to provide a unique insight into the relationship between geometry and energy, allowing the identification of explicit atoms and energies contributing towards torsional barriers within various systems. The IQA energies can be modelled to within 2.6% accuracy, as shown for a series of small systems including weakly bound complexes. The energies also allow an interpretation of how an atom feels its surrounding environment through intra-atomic, covalent and electrostatic energetic descriptions, which typically are seen to converge within a ~7 - 8 A horizon radius around an atom or small system. These energy convergence results are particularly relevant to tackling the transferability theme within force field development. Where energies are seen to converge, a proximity limit on the geometrical description needed for a transferable energy model is defined. Finally, the FFLUX force field is validated through successfully optimising distorted geometries of a series of small molecules, to near-ab initio accuracy.

Quantum Chemical Topology (QCT) descriptors, calculated from ab initio wave functions, have been utilised to model pKa and mutagenicity for data sets of pharmaceutically relevant compounds. The pKa of a compound is a pivotal property in both life science and chemistry since the propensity of a compound to donate or accept a proton is fundamental to understanding chemical and biological processes. The prediction of mutagenicity, specifically as determined by the Ames test, is important to aid medicinal chemists select compounds avoiding this potential pitfall in drug design. Carbocyclic and heterocyclic aromatic amines were chosen because this compounds class is synthetically very useful but also prone to positive outcomes in the battery of genotoxicity assays.The importance of pKa and genotoxic characteristics cannot be overestimated in drug design, where the multivariate optimisations of properties that influence the Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) profiles now features very early on in the drug discovery process.Models were constructed using carboxylic acids in conjunction with the Quantum Topological Molecular Similarity (QTMS) method. The models produced Root Mean Square Error of Prediction (RMSEP) values of less than 0.5 pKa units and compared favourably to other pKa prediction methods. The ortho-substituted benzoic acids had the largest RMSEP which was significantly improved by splitting the compounds into high-correlation subsets. For these subsets, single-term equations containing one ab initio bond length were able to accurately predict pKa. The pKa prediction equations were extended to phenols and anilines.Quantitative Structure Activity Relationship (QSAR) models of acceptable quality were built based on literature data to predict the mutagenic potency (LogMP) of carbo- and heterocyclic aromatic amines using QTMS. However, these models failed to predict Ames test values for compounds screened at GSK. Contradictory internal and external data for several compounds motivated us to determine the fidelity of the Ames test for this compound class. The systematic investigation involved recrystallisation to purify compounds, analytical methods to measure the purity and finally comparative Ames testing. Unexpectedly, the Ames test results were very reproducible when 14 representative repurified molecules were tested as the freebase and the hydrochloride salt in two different solvents (water and DMSO). This work formed the basis for the analysis of Ames data at GSK and a systematic Ames testing programme for aromatic amines. So far, an unprecedentedly large list of 400 compounds has been made available to guide medicinal chemists. We constructed a model for the subset of 100 meta-/para-substituted anilines that could predict 70% of the Ames classifications. The experimental values of several of the model outliers appeared questionable after closer inspection and three of these have been retested so far. The retests lead to the reclassification of two of them and thereby to improved model accuracy of 78%. This demonstrates the power of the iterative process of model building, critical analysis of experimental data, retesting outliers and rebuilding the model.

To understand the mechanism of a pressure-induced structural phase transition, it is important to know which bonding changes lead to the stabilization of the new structure. A useful approach in this regard is the quantum chemical topology, which provides a large variety of indicators for the characterization of interatomic interactions. In this work, a number of topological indicators are used to analyze the bonding changes during the pressure-induced phase transition from the cubic diamond (cd) to the β-Sn-type structure of the elements of the 14th group of the periodic table. The ability of these indicators to reflect the presence of the cd → β-Sn transition in experiment for Si, Ge and Sn and its absence for carbon is investigated. Furthermore, the effect of pressure on the interatomic interactions in the cd- and β-Sn-type structures is examined. It is observed that the energy change along the cd → β-Sn transformation pathway correlates with the evolution of certain parameters of the electron density and the electron localizability indicator (ELI-D). Accordingly, criteria of structural stability were formulated based on characteristics of interatomic interactions. These results can serve as guidelines for the investigation of other solid-state phase transformations by the topological methods.

To understand the mechanism of a pressure-induced structural phase transition, it is important to know which bonding changes lead to the stabilization of the new structure. A useful approach in this regard is the quantum chemical topology, which provides a large variety of indicators for the characterization of interatomic interactions. In this work, a number of topological indicators are used to analyze the bonding changes during the pressure-induced phase transition from the cubic diamond (cd) to the β-Sn-type structure of the elements of the 14th group of the periodic table. The ability of these indicators to reflect the presence of the cd → β-Sn transition in experiment for Si, Ge and Sn and its absence for carbon is investigated. Furthermore, the effect of pressure on the interatomic interactions in the cd- and β-Sn-type structures is examined. It is observed that the energy change along the cd → β-Sn transformation pathway correlates with the evolution of certain parameters of the electron density and the electron localizability indicator (ELI-D). Accordingly, criteria of structural stability were formulated based on characteristics of interatomic interactions. These results can serve as guidelines for the investigation of other solid-state phase transformations by the topological methods.