With quantum theory, who needs drugs?

1. With quantum theory, who needs drugs? UnCoRe 2007 Alaesa Hearn1; Shabana Khan2; Mohamed A. Zohdy, PhD3, 1Texas A&M Corpus Christi, 2Cal Pomona, 3Oakland University METHODS – PHARMACEUTICAL SIDE RESULTS METHODS – NEURAL NETWORK SIDE ABSTRACT Modern drug discovery process involves mapping pharmaceutical knowledge about target proteins and ligands as well as sophisticated computer science data mining. This process produces the so-called hit to lead which is followed by lead optimization to take the drug to clinical trials. In this project, we have made contributions to both the pharmaceutical and computer science domains which includes: 1. Generalized data structure for drug effectiveness descriptors, 2. Novel neural network evolved from supervised and unsupervised learning to narrow down the choice of effective drugs, 3. Applied our methods preliminary cancer-causing proteins with reasonable success. MOTIVATION ALIGNMENT 2 - Unsupervised Neural Network: 2D The target proteins this project focused on are all involved in various forms of cancer. The networks developed in this project are based on the competitive design. This selects the node most similar to the given input, and also gives weight to a mathematically designated neighborhood Akt1 JAK2 DHFR If a new disease suddenly emerges, the current system of drug discovery will take years to develop a cure. Our system is intended to reduce the response time dramatically. The protein at the right is dihydrofolatereductase (DHFR). It is shown here with ligands. It is crucial in cell division and proliferation. We trained our neural network using measurements of various conformations of an inhibitor molecule of this protein. The above diagrams show a neural network with a winning node 13, and a circular neighborhood of radius 1 (left) and a rhombus-shaped neighborhood (right). Additional features were then added to the basic structure to adjust the efficiency and effectiveness of the various networks. ALIGNMENT 3 - Unsupervised Neural Network: 3D The Drug Discovery Process: RESULTS The network before training. A 4-by-5 Self Organizing Feature Map This research is based on many interacting fields: • CONCLUSIONS • In this time-constrained research, we have learn and applied so-called rational drug discovery, in which computer science is essential to complement pharmaceutical analysis to produce hits (promising molecules) and leads (proven effective molecules). The core of this process utilizes small molecule descriptions, which can be geometric, chemical, physical, or a computer learning algorithm. We focuses on a class of neural networks that are based on competitiveness and self-organizing. We then applied the neural network appropriately to one specific protein, DHFR, and have been able to show preliminary effectiveness of extended neural learning to binding and reaction mechanisms of many possible inhibitor ligands. RESEARCH OBJECTIVES • Pharmacology Side • Drug targets (macro molecules, key proteins) • Ligands (micro molecules, drug compounds) • Consider the docking, binging, and reacting of the targets and ligands • Focus on proteins involved in various types of cancer • State-of-the-art databases • Neural Network Side • Develop new neural network paradigms by combining known features into new structures, i.e. Self-Organizing Vector Quantization • Use the competitive network at the core, and add features of other • Apply neural networks to discover effective drug molecules. We are drowning in information but starved for knowledge. - John NaisbittAs scientific research advances, the collective pool of data grows exponentially. All of these facts must be collected and organized in order to be useful information. However, the sheer quantity of data presents difficulties in searching, integrating, and applying this knowledge. Neural networks are among the best tools available for recognizing and analyzing relationships in vast amounts of information. ALIGNMENT 1 - Supervised Neural Network: 2D REFERENCES Annema, Anne-Johan. Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling, and Analog Implementation. Norwell, MA: Kluwer Academic Publishers, 1995. Yi, Zhang, and K.K Tan. Convergence Analysis of Recurrent Neural Networks. Norwell, MA: Kluwer Academic Publishers, 2004. Neelakanta, Perambur S., and Dolores F. De Groff. Neural Network Modeling. Boca Raton: CRC Press, 1994. Wade, L.G. Organic Chemistry. 3rd ed. Upper Saddle River, NJ: Prentice Hall, 1995. Warmuth, Manfred K. "Active learning with support vector machines in the drug discovery process." Journal of chemical information and computer sciences 43.2 (2003): 667. 14 June 2007 <http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/ci025620t>. FUTURE RESEARCH OBJECTIVES • Drug Delivery • Drug interaction • Protein interaction • Personalized medicine • Drugs for developing countries • Orphan diseases • Drug addiction Chemoinformatics Descriptors