Pharma IQ Live: Transforming Drug Discovery through Artificial Intelligence (AI) 2022

Pharma IQ Live: Transforming Drug Discovery through Artificial Intelligence (AI) is organized by Pharma IQ and will be held from Jul 05 - 06, 2022.

Who should attend?
Pharma IQ Live is designed for Heads / General Managers / Directors of  Artificial Intelligence, Data, Drug Discovery, Discovery Informatics, Research and Development, Science & Technology

Overview:
Leveraging data to reduce the cost, time to market and failure rate of the traditional drug discovery process
It is a commonly quoted statistic that the average cost to bring a new drug to market is 2.6bn, and that only 10% of drug candidates make it to market after phase 1 trials.

Given its potential to spot patterns that would take researches significantly longer to do, Artificial Intelligence has the potential to both accelerate and reduce the cost of discovering new drugs.

With that in mind, PharmaIQ Live: Transforming Drug Discovery through AI will be focused on:
• Target identification – combing existing data to identify potential drug targets
• Compounds – identifying existing compounds that might prove useful, ruling out those with undesirable characteristics and predicting the properties of potential compounds
• Data integration – effectively integrating data from disparate sources such as patient data, research papers to identify patterns and opportunities
• Enhancing efficiency – by removing the burden of high volume, low value tasks in the lab
• Training – upskilling existing drug development scientists on how best to use AI to best effect

Pharma IQ Live: Utilizing AI in Drug Discovery will be focused on:
• Technically integrating and apply AI algorithms to enhance trial design, improve patient selection and retention and achieve more precise pharmacovigilance studies.
• Grasping how AI can fundamentally change the paradigm of clinical trial execution and identify key areas where AI can positively impact your drug development decision making.
• Harnessing AI to extract maximum value to achieve regulatory success and accelerate novel therapeutic products to market
• Assessing the strategic role AI is playing in the emerging “data-centric” model of innovation
• Providing an increased understanding of AI mechanics with a focus on AI-driven tools and approaches
• Improving decision-making through analysis of data sets to generate novel insights and identify novel drug candidates
• Eliminating blind spots in research and generate insights from thousands of unrelated data sources