An alternative approach is reported to compute property fields based on similarity indices of drug molecules that have been brought into a common alignment. The fields of different physicochemical properties use a Gaussian-type distance dependence, and no singularities occur at the atomic positions. Accordingly, no arbitrary definitions of cutoff limits and deficiencies due to different slopes of the fields are encountered. The fields are evaluated by a PLS analysis similar to the CoMFA formalism. Two data sets of steroids binding to the corticosteroid-binding-globulin and thermolysin inhibitors were analyzed in terms of the conventional CoMFA method (Lennard-Jones and Coulomb potential fields) and the new comparative molecular similarity indices analysis (CoMSIA). Models of comparative statistical significance were obtained. Field contribution maps were produced for the different models. Due to cutoff settings in the CoMFA fields and the steepness of the potentials close to the molecular surface, the CoMFA maps are often rather fragmentary and not contiguously connected. This makes their interpretation difficult. The maps obtained by the new CoMSIA approach are superior and easier to interpret. Whereas the CoMFA maps denote regions apart from the molecules where interactions with a putative environment are to be expected, the CoMSIA maps highlight those regions within the area occupied by the ligand skeletons that require a particular physicochemical property important for activity. This is a more significant guide to trace the features that really matter especially with respect to the design of novel compounds.

The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.

The prevalent occurrence of herbicide resistant weeds increases the necessity for new site of action herbicides for effective control as well as to relax selection pressure on the known sites of action. As a consequence, interest increased in the unexploited molecule cinmethylin as a new solution for the control of weedy grasses in cereals. Therefore, the mechanism of action of cinmethylin was reevaluated. We applied the chemoproteomic approach cellular Target Profiling™ from Evotec to identify the cinmethylin target in Lemna paucicostata protein extracts. We found three potential targets belonging to the same protein family of fatty acid thioesterases (FAT) to bind to cinmethylin with high affinity. Binding of cinmethylin to FAT proteins from Lemna and Arabidopsis was confirmed by fluorescence-based thermal shift assay. The plastid localized enzyme FAT plays a crucial role in plant lipid biosynthesis, by mediating the release of fatty acids (FA) from its acyl carrier protein (ACP) which is necessary for FA export to the endoplasmic reticulum. GC-MS analysis of free FA composition in Lemna extracts revealed strong reduction of unsaturated C18 as well as saturated C14, and C16 FAs upon treatment with cinmethylin, indicating that FA release for subsequent lipid biosynthesis is the primary target of cinmethylin. Lipid biosynthesis is a prominent target of different herbicide classes. To assess whether FAT inhibition constitutes a new mechanism of action within this complex pathway, we compared physiological effects of cinmethylin to different ACCase and VLCFA synthesis inhibitors and identified characteristic differences in plant symptomology and free FA composition upon treatment with the three herbicide classes. Also, principal component analysis of total metabolic profiling of treated Lemna plants showed strong differences in overall metabolic changes after cinmethylin, ACCase or VLCFA inhibitor treatments. Our results identified and confirmed FAT as the cinmethylin target and validate FAT inhibition as a new site of action different from other lipid biosynthesis inhibitor classes.

Mutual binding between a ligand of low molecular weight and its macromolecular receptor demands structural complementarity of both species at the recognition site. To predict binding properties of new molecules before synthesis, information about possible conformations of drug molecules at the active site is required, especially if the 3D structure of the receptor is not known. The statistical analysis of small-molecule crystal data allows one to elucidate conformational preferences of molecular fragments and accordingly to compile libraries of putative ligand conformations. A comparison of geometries adopted by corresponding fragments in ligands bound to proteins shows similar distributions in conformations space. We have developed an automatic procedure that generates different conformers of a given ligand. The entire molecule is decomposed into its individual ring and open-chain torsional fragments, each used in a variety of favorable conformations. The latter ones are produced according to the library information about conformational preferences. During this building process, an extensive energy ranking is applied. Conformers ranked as energetically favorable are subjected to an optimization in torsion angle space. During minimization, unfavorable van der Waals interactions are removed while keeping the open-chain torsion angles as close as possible to the experimentally most frequently observed values. In order to assess how well the generated conformers map conformation space, a comparison with experimental data has been performed. This comparison gives some confidence in the efficiency and completeness of this approach. For some ligands that had been structurally characterized by protein crystallography the program was used to generate sets of some 10 to 100 conformers. Among these, geometries are found that fall convincingly close to the conformations actually adopted by these ligands at the binding site.