The binding energies and Gibbs free energies ofbinding for melatonin and other indole-,naphthalene-, and benzene-moiety ligands totrans-membrane sections of the seven-helix humanmelatonin receptor MT1 have been calculated atthe ab initio and semi-empirical levels using GAMESS, and with molecular mechanics using Sybyl. A linear relationship was found between the Sybyl-calculated binding energies and theexperimentally-determined pKi values, and alinear fit of calculated HINT scores to theexperimentally-measured ΔG values givesdeltaG = -0.00263Hscore - 9.477, in kcal/mol. In addition, the interactions between individual residues in MT1 and various ligands were examined to determine why some ligands bind more strongly to MT1 than others. The magnetic circular dichroism spectra of melatonin bound tofragments of MT1 were also calculated, in theCNDO/S-D approximation.

No / A description of the ab initio quantum chemistry package GAMESS-UK is presented. The package offers a wide range of quantum mechanical wavefunctions, capable of treating systems ranging from closed-shell molecules through to the species involved in complex reaction mechanisms. The availability of a wide variety of correlation methods provides the necessary functionality to tackle a number of chemically important tasks, ranging from geometry optimization and transition-state location to the treatment of solvation effects and the prediction of excited state spectra. With the availability of relativistic ECPs and the development of ZORA, such calculations may be performed on the entire Periodic Table, including the lanthanides. Emphasis is given to the DFT module, which has been extensively developed in recent years, and a number of other, novel features of the program. The parallelization strategy used in the program is outlined, and detailed speedup results are given. Applications of the code in the areas of enzyme and zeolite catalysis and in spectroscopy are described.

Methods to automatically extract and validate data from the chemical literature in legacy formats to machine-understandable forms are examined. The work focuses of three types of data: analytical data reported in articles, computational chemistry output files and crystallographic information files (CIFs). It is shown that machines are capable of reading and extracting analytical data from the current legacy formats with high recall and precision. Regular expressions cannot identify chemical names with high precision or recall but non-deterministic methods perform significantly better. The lack of machine-understandable connection tables in the literature has been identified as the major issue preventing molecule-based data-driven science being performed in the area. The extraction of data from computational chemistry output files using parser-like approaches is shown to be not generally possible although such methods work well for input files. A hierarchical regular expression based approach can parse > 99.9% of the output files correctly although significant human input is required to prepare the templates. CIFs may be parsed with extremely high recall and precision, contain connection tables and the data is of high quality. The comparison of bond lengths calculated by two computational chemistry programs show good agreement in general but structures containing specific moieties cause discrepancies. An initial protocol for the high-throughput geometry optimisation of molecules extracted from the CIFs is presented and the refinement of this protocol is discussed. Differences in bond length between calculated and experimentally determined values from the CIFs of less than 0.03 Angstrom are shown to be expected by random error. The final protocol is used to find high-quality structures from crystallography which can be reused for further science.

While computational chemistry methods have a wide range of applications within the set of traditional physical sciences, very little is being done in terms of expanding their usage into other areas of science where these methods can help clarify research questions. One such promising field is Forensic Science, where detailed, rapidly acquired sets of chemical data can help in decision-making at a crime scene. As part of an effort to create a database that fits these characteristics, the present work makes use of computational chemistry methods to increase the information readily available for the rapid identification and scheduling of drugs to the forensic scientist. Ab initio geometry optimizations, vibrational spectra calculations and ESI-MS fragmentation prediction of a group of common psychedelics are here presented. In addition, we describe an under development graphical user interface to perform ab initio calculations using the GAMESS software package in a more accessible manner. Results show that the set of theoretical techniques here utilized, closely approximate experimental data. Another aspect covered in this work is the implementation of a boiling point estimation method based on group contributions to generate chemical dispersion areas with the ALOHA software package. Once again, theoretical results showed to be in agreement with experimental boiling point values. A computer program written to facilitate the execution of the boiling point estimation method is also shown.