ABSTRACT

1
With quantum theory, who needs drugs?
UnCoRe 2007
Alaesa Hearn1 Shabana Khan2 Mohamed A. Zohdy,
PhD3, 1Texas AM Corpus Christi, 2Cal Pomona,
3Oakland University

• 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

METHODS PHARMACEUTICAL SIDE
RESULTS
METHODS NEURAL NETWORK SIDE
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 dihydrofolate
reductase (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 lthttp//pubs3.acs.
org/acs/journals/doilookup?in_doi10.1021/ci025620
tgt.

• FUTURE RESEARCH OBJECTIVES
• Drug Delivery
• Drug interaction
• Protein interaction
• Personalized medicine
• Drugs for developing countries
• Orphan diseases
• Drug addiction

Chemoinformatics Descriptors