Relationships between soil sorption normalized to organic carbon (Koc) and molecular properties of 71 herbicides were examined. The Koc values were obtained from the literature. Various molecular properties were calculated by quantum mechanical methods using molecular modeling software. The quantitative structure activity relationship (QSAR) models based on four molecular properties, van der Waals volume (VDWv), molecular polarizability (α), dipole moment (μ), and energy of highest occupied molecular orbital (eHOMO), together accounted for 70% of the variation in Koc. Herbicides were broadly divided into six families based on structural similarities, and separate equations were established for each group. The three descriptors, VDWv, α, and μ, along with either energy of lowest unoccupied molecular orbital (eLUMO), or electrophilic superdelocalizability (SE), or eHOMO appeared to be determinants and accounted for 82 to 99% of the variation in Koc. Applicability of these models was tested for one herbicide analogue and 10 metabolites. The QSAR models appear to be specific to structurally similar chemicals. The QSAR models could be developed to predict Koc of structurally similar compounds even before they are synthesized or for some of the metabolites of existing herbicides. Models of this type can also be developed to create priority lists for testing, so that time, money, and efforts can be focused on the potentially most hazardous chemicals.

A convergence of research effort in a number of scientific disciplines in the early 1980s resulted in a rapid expansion of knowledge of the structure and function of the photosynthetic reaction center in bacteria and higher plants. The structure of the reaction center from photosynthetic bacteria was determined by X-ray analysis. The herbicide binding protein (the D1 protein) was identified by photoaffinity labelling and found to be an integral part of the photosynthetic reaction center complex in higher plants. Studies using herbicide-resistant mutants enabled the location of the herbicide binding niche on D1 to be determined. Quantitative Structure Activity Relationships (QSAR) of families of inhibitors and their effect on photosynthetic electron transport helped elucidate the nature of the interaction between inhibitors and receptor. Binding appeared to be predominantly hydrophobic with hydrogen bonding also having an important role. Studies with a series of highly potent inhibitors, the 2-cyanoacrylates, identified certain steric constraints in the interaction of these molecules with the binding site. The activity of these inhibitors was particularly sensitive to minor structural change and they proved to be useful probes of receptor topography. The results of structure-activity studies of the 2-cyanoacrylates combined with a refined knowledge of the three-dimensional structure of the inhibitor binding site has enabled computer-based molecular modelling of interactions of these inhibitors with the receptor. The spatial arrangement of the inhibitor functional groups within the binding domain was shown to be a critical factor in determining binding affinity.

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Although there are many glutamate receptors in the retina, 2-amino-4-phosphonobutyrate (L-AP4) is an agonist that acts selectively at metabotropic glutamate receptors (mGluR6) of ON bipolar cells. We explored the properties of agonists that activate this receptor. The effects of various glutamate analogs on the b-wave of the electroretinogram (ERG) were used as a measure of their activity. Conformational comparisons among agonists suggest that ligands in an extended conformation preferentially bind to the ON bipolar synaptic receptor. But this property is insufficient to explain the selectivity of mGluR6 because some inactive glutamate analogs could also match this extended conformation. Comparative molecular field analysis (CoMFA) was used to compare the electrostatic and steric potentials of agonists with their action at the ON bipolar synapse. Steric potentials beneath a plane defined by the three putative binding sites plays a key role in determining agonist activity. The CoMFA model was used to predict the activity of glutamate analogs and correlations between predicted and measured activity support the model.