What is not universally known is the quiet revolution that has taken place in early-stage drug research, especially regarding patents and IP. Historically, this has been a highly manual process. Sifting through patent applications to see if a specific molecular structure has been previously registered is critical, as is manually researching relevant research and peer-reviewed papers to investigate if previous studies concur with current investigations. As a result, the process can take trained researchers months to complete. Add to that the chance of human errors, such as the misfiling of data, and initial progress can be extremely time-consuming.

Just a couple of years ago, early-stage molecular investigation would entail hours of research, wading through published data, field trials and research documents in pursuit of relevant information. A search for a clinical study to support any initial findings was akin to finding the holy grail in pharmaceutical terms.

This administrative leviathan was crying out to be automated. We just required the vision and technology to make it happen. Organisations need to fail fast and fail cheap, harnessing the power of drug analysis to overhaul the current rate of research, while rapidly seeking out corresponding areas of innovation.

COVID-19 was an impetus for change; a call to action for pharmaceutical firms to innovate and to establish new processes and methods that were not previously required – or not with the same sense of urgency. The effect of this innovation has been momentous. Previously, the first stage of drug research took between six and nine months to complete. Now we can complete the process in just 20 minutes, using our bespoke AI software analytics platform.

PatSnap has worked in life sciences since 2016, developing a business model based on machine learning that can analyse global medical documentation such as molecular structures, registered patents, research papers and technical documents. Data can be easily refined, filtered and sorted by clinical trial data and agency, such as REACH, the US Food and Drug Administration (FDA), and the China Food and Drug Administration (CFDA) – now the National Medical Products Administration (NMPA) – and more. By condensing this huge amount of data, we enable deep, meaningful analysis of a particular chemical area, helping manufacturers to quickly map the innovation process from investment to commercialisation.

We have proven that what used to take years to deliver, with regards to drug efficacy and trials, can now be done in a fraction of that time. Very few pharmaceutical businesses now use manpower to deliver vital first steps in drug development. Indeed, many now use proprietary software that collates data from thousands of sources and presents it as vital management information. It is the scale of automation that is the driving force behind the new wave of rapid drug discovery and development.

The pharma sector is now predominantly on board with the adoption of this type of analytical technology. But it has only been since the outbreak of COVID-19 that the medium cap and large corporations have put technology investment at the top of their budget lists. According to data from the Pistoia Alliance (a global life sciences member organisation) AI, machine learning and blockchain are critical priority areas for investment as the industry comes to realise that emerging tech investment will enable it to better address future public health crises. This trend is reiterated by the fact that earlier in 2021, PatSnap raised $300m for future development of its AI-driven R&D and IP analytics capability.

The next ten years will be all about open, cross-border collaboration focused on meeting future challenges. In our work with pharma businesses, we have seen how our platform has forged a bridge between ‘drug development’ and ‘business development’ departments. What was previously often a chasm has now been filled with a shared dialogue created through the pooling of valuable information. Historically, we have observed, poor internal communication has tended to be one of the more significant brakes on faster drug development.

It is deeply heartening to witness how new technologies like ours have allowed a highly authentic level of collaboration between bench-level developments, marketing consultants and c-suite managers across the pharmaceutical industry. Indeed, from our own experience of working with the global pharma companies involved in the successful development of COVID-19 vaccines, for example, the contribution of AI and machine learning cannot be overestimated.

Looking ahead to the next decade, we see pharma and biotech companies acting collaboratively, much in the same way as software companies did in the nineties. We are already seeing this shift, driven by new technology adoption and the emergence of platforms, which is very exciting. Big pharma corporations are assessing the advantages of a more collaborative, open style that will open IP access rights, allowing smaller enterprises to use their platforms and knowledge to develop and deliver new drug therapies, much in the same way that the various COVID-19 vaccines were developed across the world.

This model will happen in biotech and pharma over the coming years. A similar model is emerging within life sciences. Of course, we cannot ignore the fact that COVID-19 has been one of the world’s most serious pandemics. But it has also brought about much-needed change and provided hitherto unimagined opportunities for innovation, heralding the dawn of a new era that will finally see the emergence of tailored drugs, personalised medicine and reduced side effects for people across the world.

To an extent, it is the capability of machine learning that has enabled innovation at such a scale. Technology’s ability to grind down millions of pages of data into useful information in a matter of hours, not months, is helping to enable a new age of effective vaccines and wholly bespoke and tailored drug therapies.

In an emerging global economy, intellectual property is a high priority to many organisations, as new technologies, personalised medicine and targeted therapies promise hopeful cures for troubling illnesses. Now more than ever, the ease of data searches could deliver significant financial and strategic gains for the industry across the board.

Ray Chohan is Co-founder and Vice President of New Ventures at PatSnap, a UK company that develops AI-driven R&D and IP analytics for the global life sciences sector