This is part of the Pistoia Alliance Collaborate to Innovate Virtual Conference Week, October 19-23, 2020.  For more information about related events, please visit our online calendar.

Summary

In this presentation we will discuss the challenges and opportunity of using data & predictive tools to guide drug discovery and pre-clinical development. We will use examples from recent projects, e.g.:

• In-silico COVID-19 model

• Drug-drug Interaction predictors

• Chronic pancreatitis drug repurposing

• Using big data to may reduce the amount of animal testing needed in drug development.

Those projects should demonstrate how big data & In-silico experimentations augment traditional research approaches (i.e.: in-vitro & in-vivo) and open new avenues, that were difficult or impossible to explore before.

Finally, we will address, what we believe, are today’s biggest challenges:

1. The not-so-sexy but-oh-so important “Sourcing & preparing” life sciences data for further analysis or “data janitoring”
2. The creation of tools & services that are “good enough to be useful” and “simple enough” so that the masses can use them

Learning Objectives

At the conclusion of this session, participants should be able to:

• Understand the challenges and value of sourcing various data, enriching them, and preparing them for further consumption

• Understand the value of using in-silico models to augment the researcher’s workflow

• Understand the value of using tools that are “good enough” vs “complex and comprehensive”

Featured Topics

• Sourcing & preparing” life sciences data for further analysis or “data janitoring”

• Creation of tools & services that are “good enough to be useful” and “simple enough” so that the masses can use them

• The use of big data and in-silico models to augment the researchers’ workflow

Sponsored by: 

Thibault Géoui Senior Director Discovery Biology & Predictive Risk Management, Elsevier

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