Aug
13
Tue
2013
Invited Talk: A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry @ Sathyam Hall
Aug 13 @ 11:15 am – 11:40 am

kalKal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA


A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry

With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways.  Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists.  As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery.  While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories.  Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site.  As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty.  In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases.  We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase.  We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.

 

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.