This thesis explores new ways to integrate manufacturing processes information in to design phases. Through the use of design for manufacturing (DfM) concept, and looking at relations between its potential application in architectural production and its implementation using digital manufacturing technologies, the author implemented a DfM model that varies from previous models by incorporated learning in the process. This process was based on the incremental development and refinement of design heuristics and metrics. The DfM model developed in this research is a process model to be implemented as a framework within educational settings. The proposed model is based in two basic strategies; first a process description in the form of alternative design strategies; and second, the implementation of design heuristics and design metrics. Subsequently, the author tested and refined the model using a sequence of case studies with students. In the final stage, the research evaluated and further developed the DfM model in a component design case study. The general purpose in performing this case studies sequence was to test the proposed DfM model. The second objective was to refine the DfM model by capturing knowledge from the case studies. As a summary, this research conceptualizes from this top-down development approach to create a design for manufacturing model that integrates design and construction in architecture, based on three possible applications fields; DfM teaching approaches development, design processes improvement; and DfM methods development. The final purpose is to provide better foundational constructs for architectural education and to improve teaching approaches that integrate design and manufacturing.

In recent years, the potential benefits of high-throughput virtual screening to the drug discovery community have been recognized, bringing an increase in the number of tools developed for this purpose. These programs have to process large quantities of data, searching for an optimal solution in a vast combinatorial range. This is particularly the case for protein-ligand docking, since proteins are sophisticated structures with complicated interactions for which either molecule might reshape itself. Even the very limited flexibility model to be considered here, using ligand conformation ensembles, requires six dimensions of exploration - three translations and three rotations - per rigid conformation. The functions for evaluating pose suitability can also be complex to calculate. Consequently, the programs being written for these biochemical simulations are extremely resource-intensive. This work introduces a pure computer science approach to the field, developing techniques to improve the effectiveness of such tools. Their architecture is generalized to an abstract pattern of nested layers for discussion, covering scoring functions, search methods, and screening overall. Based on this, new stratagems for molecular docking software design are described, including lazy or partial evaluation, geometric analysis, and parallel processing implementation. In addition, a range of novel algorithms are presented for applications such as active site detection with linear complexity (PIES) and small molecule shape description (PASTRY) for pre-alignment of ligands. The various stratagems are assessed individually and in combination, using several modified versions of an existing docking program, to demonstrate their benefit to virtual screening in practical contexts. In particular, the importance of appropriate precision in calculations is highlighted.

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