The recent race to market for COVID-19 vaccines has only accentuated the value of alliances as companies with core vaccine capabilities turned to external partnerships to leverage the value of nascent mRNA technology. …

Today, omics refers to an array of technologies used to measure and functionally characterise different biological molecules in cells or tissues. These technologies collectively provide a comprehensive view of an individual’s genetic makeup that is critical to the development of personalised medicines. They drive the development of diagnostic and therapeutic…

However, NLP in drug discovery is not a monolithic concept. There are several possible approaches, each of which may be particularly suited for specific applications. Moreover, any comprehensive solution for integrated enterprise-wide analysis will likely require a blended or hybrid NLP approach. So here’s a quick dive into some of…

AI technologies are currently the most disruptive trend in the pharmaceutical industry. Over the past year, we have quite extensively covered the impact these intelligent technologies can have on conventional drug discovery and development processes. We charted how AI/ML technologies came to be a core component of drug discovery and…

AI/ML technologies can be notoriously data-hungry with the broader concern being that certain domains may struggle to acquire and integrate enough big data for productive analytics. In the case of genomics, the concern is quite the opposite. The continuing evolution of 21st century NGS technologies has opened the floodgates for…

Natural Language Processing is a multidisciplinary field and over the years several models and algorithms have been successfully used to parse text. ML approaches have been central to NLP development with many of them particularly focussing on a technique called sequence-to-sequence learning (Seq2seq). Deep Neural Networks First introduced by Google in 2014, Seq2seq…

NLP challenges can be classified into two broad categories. The first category is linguistic and refers to the challenges of decoding the inherent complexity of human language and communication. We covered this category in a recent Why Is NLP Challenging? article. The second is data-related and refers to some of…

In our previous blog, we noted how the increasing utilization of AI across different phases of the drug discovery process has proven the strategic value of the technology in addressing some of the core efficiency and productivity challenges involved. As a result, AI in drug discovery and development has finally…

A 2021 Natural Language Processing (NLP) industry survey found a significant year-on-year increase in NLP budgets across businesses from different industries, company sizes, geographic locations and stages of NLP adoption. According to the study, the top four industries leading NLP adoption were healthcare, technology, education, and financial services. …