Abstract: Phenyl acetamide compounds are described, including compounds of Formula I: or a solvate, hydrate or pharmaceutically acceptable salt thereof; wherein R3-R6, R11, B, Y and W are set forth in the specification. The compounds of the invention are potent inhibitors of proteases, especially trypsin-like serine proteases, such as thrombin and factor Xa. Compositions for inhibiting loss of blood platelets, inhibiting formation of blood platelet aggregates, inhibiting formation of fibrin, inhibiting thrombus formation, and inhibiting embolus formation are described. Other uses of compounds of the invention are as anticoagulants either embedded in or physically linked to materials used in the manufacture of devices used in blood collection, blood circulation, and blood storage, such as catheters, blood dialysis machines, blood collection syringes and tubes, blood lines and stents. Additionally, the compounds can be detectably labeled and employed for in vivo imaging of thrombi.

Abstract: A system, method, and computer program product for fast and efficient searching of large virtual combinatorial libraries based on a fitness function. According to the method of the present invention, a first set of N reagent combinations are selected, for example, at random, from a virtual combinatorial library. Each reagent combination in the first set is then enumerated to produce a first set of enumerated compounds. M number of compounds of the first set of enumerated compounds are selected based on the fitness function. The M compounds are then deconvoluted into reagents to generate a focused library. Substantially every reagent combination associated with the focused library is enumerated to produce a second set of enumerated compounds. K number of compounds of the second set of enumerated compounds are then selected based on the fitness function. These K compounds represent a near optimal selection of compounds based on the fitness function.

Abstract: A system, method, and computer program product for visualizing and interactively analyzing data relating to chemical compounds. A user selects a plurality of compounds to map, and also selects a method for evaluating similarity/dissimilarity between the selected compounds. A non-linear map is generated in accordance with the selected compounds and the selected method. The non-linear map has a point for each of the selected compounds, wherein a distance between any two points is representative of similarity/dissimilarity between the corresponding compounds. A portion of the non-linear map is then displayed. Users are enabled to interactively analyze compounds represented in the non-linear map.

Abstract: A method, system, and computer product is presented for mapping a set of patterns into an m-dimensional space so as to preserve relationships that may exist between these patterns. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping process based on subset refinements. A set of n attributes are determined for each pattern, and one or more neural networks or other supervised machine learning techniques are then trained in accordance with the mapping produced by the iterative process. Additional input patterns not in the subset are mapped into the m-dimensional space by determining their n input attributes and using the neural networks in a feed-forward (prediction) mode.

Abstract: The present invention provides a method for functionally classifying a protein that is capable of unfolding due to a thermal change. The method comprises screening one or more of a multiplicity of different molecules for their ability to shift the thermal unfolding curve of the protein, wherein a shift in the thermal unfolding curve indicates that the molecule binds to the protein or affects the stability in a measurable way; generating an activity spectrum for the protein wherein the activity spectrum reflects a set of molecules, from the multiplicity of molecules, that shift the thermal unfolding curve, of the protein and therefore are ligands that bind to the protein, comparing the activity spectrum for the protein to one or more functional reference spectrum lists; and classifying the protein according to the set of molecules in the multiplicity of different molecules that shift the thermal unfolding curve of the protein.

Abstract: The present invention is directed to a compound of Formula I: wherein A, R1, and R2 are defined herein. The present invention is also directed to compositions comprising compounds of Formula I, methods of using compounds of Formula I, and methods of making compounds of Formula I.

Abstract: The invention provides for in silico analysis of a virtual combinatorial library. Mapping coordinates for a training subset of products in the combinatorial library, and features of their building blocks, are obtained. A supervised machine learning approach is used to infer a mapping function ƒ that transforms the building block features for each product in the training subset of products to the corresponding mapping coordinates for each product in the training subset of products. The mapping function ƒ is then encoded in a computer readable medium. The mapping function ƒ can be retrieved and used to generate mapping coordinates for any product in the combinatorial library from the building block features associated with the product.

Abstract: A method and computer product is presented for mapping n-dimensional input patterns into an m-dimensional space so as to preserve relationships that may exist in the n-dimensional space. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping process. A set of locally defined neural networks is created, then trained in accordance with the mapping produced by the iterative process. Additional input patterns not in the subset are mapped into the m-dimensional space by using one of the local neural networks. In an alternative embodiment, the local neural networks are only used after training and use of a global neural network. The global neural network is trained in accordance with the mapping produced by the iterative process. Input patterns are initially projected into the m-dimensional space using the global neural network. Local neural networks are then used to refine the results of the global network.

Abstract: A microtiter plate system includes an integral heater. In an embodiment, the integral heater includes a heater plate. In another embodiment, the integral heater includes resistive heater wires positioned beneath and/or between the wells of a microtiter plate. In an embodiment, the microtiter plate system includes optically clear well bottoms that permit sensing and measurement of samples through the optically clear well bottoms. In an implementation, an optically clear heater is positioned beneath the optically clear well bottoms. In an alternative implementation, resistive heater wires are positioned between the wells. In an embodiment, the microtiter plate system includes a microtiter plate lid with an integral heater, which can be implemented using a heater plate, resistive wires, and the like.