About this event
The drug discovery and development process is extremely time-consuming, expensive and challenging; taking an average of 10 – 15 years to bring a new drug to market. Moreover, it is estimated that only around 10% of drugs make it to market following phase I trials. Due to the long development process and the low success rate, the cost of developing a new drug is around $2.6 billion. This has caused researchers to turn to artificial intelligence as a way to accelerate and reduce the cost of the drug discovery pipeline.
The market size of AI-enabled drug discovery is projected to reach $1.43 billion by 2024. Despite the wealth of information available about the potential of AI for the pharmaceutical industry, there is a lack of case studies available presenting data on how AI can really help revolutionise the drug discovery pipeline.
In this 3-part webinar series our pharma and academic leaders will present case studies on the success and failures of AI and machine learning innovations, to pinpoint lessons learned and new approaches. We will cover what the most pressing challenges are for companies developing and employing ML approaches and how companies can evaluate AI/ML technologies to facilitate better investment in drug discovery technologies.
Identifying novel, druggable targets for therapeutic intervention remains a priority for the pharma industry since this process, as well as, validating, and prioritizing identified targets remains an important bottleneck in drug discovery.
In this webinar we will discuss innovations in AI/ML for target discovery and what is being done to validate targets once identified. We will also cover the novel computational approaches that are aiding selection and prioritization of drug targets for future drug development.
Data from libraries of small molecules is driving new compound selection. The sheer size of libraries used to screen for new drug candidates means it is extremely size and resource consuming for researchers to review the data themselves – this is where AI and machine learning can help. These technologies allow researchers to extract insights from huge datasets, which had previously been largely inaccessible.
This webinar will cover the use of different AI and machine learning tools used to predict the properties of potential compounds and identify lead compounds from extensive small molecule libraries.
Despite advances in technology, de novo drug design has been a costly and time-consuming process for decades. AI technologies have been developed, which can design new compounds that precisely fit the structural criteria required to bind specific targets. Drug repositioning and repurposing has also peaked in interest as an alternative tool to accelerate drug discovery, it can be used to quickly detect existing drugs that can be utilised to fight against emerging diseases such as COVID-19, as well as existing diseases.
In this webinar we will discuss the applications of different AI technologies and explore lessons learned in the applications of AI to de novo drug design and drug repurposing. We will also discuss whether the best approach to revolutionise the drug discovery pipeline in the future lies within AI led de novo drug design or drug repurposing.
Joining for Q&A
Front Line Genomics is a genomics-focused media company, with a social mission to deliver the benefits of genomics to patients faster. We organise the Festival of Genomics, digital events, reports, webinars and digital events, as well as operating a content-rich website.
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