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Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D(1) dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R(2) guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R(2) (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D(1) ligands.

In light of the chronic problem of abuse of the controlled substance cocaine, we have investigated novel approaches toward both understanding the activity of inhibitors of the dopamine transporter (DAT) and identifying novel inhibitors that may be of therapeutic potential. Our most recent studies toward these ends have made use of two-dimensional (2D) quantitative structure-activity relationship (QSAR) methods in order to develop predictive models that correlate structural features of DAT ligands to their biological activities. Specifically, we have adapted the method of genetic algorithms-partial least squares (GA-PLS) (Cho et al. J. Comput. -Aided Mol. Des., submitted) to the task of variable selection of the descriptors generated by the software Molconn Z. As the successor to the program Molconn X, which generated 462 descriptors, Molconn Z provides 749 chemical descriptors. By employing genetic algorithms in optimizing the inclusion of predictive descriptors, we have successfully developed a robust model of the DAT affinities of 70 structurally diverse DAT ligands. This model, with an exceptional q(2) value of 0.85, is nearly 25% more accurate in predictive value than a comparable model derived from Molconn X-derived descriptors (q(2) = 0.69). Utilizing activity-shuffling validation methods, we have demonstrated the robustness of both this DAT inhibitor model and our QSAR method. Moreover, we have extended this method to the analysis of dopamine D(1) antagonist affinity and serotonin ligand activity, illustrating the significant improvement in q(2) for a variety of data sets. Finally, we have employed our method in performing a search of the National Cancer Institute database based upon activity predictions from our DAT model. We report the preliminary results of this search, which has yielded five compounds suitable for lead development as novel DAT inhibitors.

Abstract-Since its inception about three decades ago, modern minimally invasive surgery has made huge advances in both technique and technology. However, the minimally invasive surgeon is still faced with daunting challenges in terms of visualization and hand-eye coordination.At the Center for Computer Integrated Surgical Systems and Technology (CISST) we have been developing a set of techniques for assisting surgeons in navigating and manipulating the three-dimensional space within the human body. In order to develop such systems, a variety of challenging visual tracking, reconstruction and registration problems must be solved. In addition, this information must be tied to methods for assistance that improve surgical accuracy and reliability but allow the surgeon to retain ultimate control of the procedure and do not prolong time in the operating room.In this article, we present two problem areas, eye microsurgery and thoracic minimally invasive surgery, where computational vision can play a role. We then describe methods we have developed to process video images for relevant geometric information, and related control algorithms for providing interactive assistance. Finally, we present results from implemented systems.

In order to circumvent limitations of sequence based methods in the process of making functional predictions for proteins, we have developed a methodology that uses a sequence-to-structure-to-function paradigm. First, an approximate three-dimensional structure is predicted. Then, a threedimensional descriptor of the functional site, termed a Fuzzy Functional Form, or FFF, is used to screen the structure for the presence of the functional site of interest (Fetrow et al., 1998; Fetrow and Skolnick, 1998). Previously, a disulfide oxidoreductase FFF was developed and applied to predicted structures obtained from a small structural database. Here, using a substantially larger structural database, we expand the analysis of the disulfide oxidoreductase FFF to the B. subtilis genome. To ascertain the performance of the FFF, its results are compared to those obtained using both the sequence alignment method BLAST and three local sequence motif databases: PRINTS, Prosite, and Blocks. The FFF method is then compared in detail to Blocks and it is shown that the FFF is more flexible and sensitive in finding a specific function in a set of unknown proteins. In addition, the estimated false positive rate of function prediction is significantly lower using the FFF structural motif, rather than the standard sequence motif methods. We also present a second FFF and describe a specific example of the results of its whole-genome application to D. melanogaster using a newer threading algorithm. Our results from all of these studies indicate that the addition of three-dimensional structural information adds significant value in the prediction of biochemical function of genomic sequences.