Intelligent Health.tech Issue 22 | Page 43

I N D U S T R Y I N V E S T I G A T I O N
R & D managers reporting decisions at key programme milestones to medical writers drafting filings for health authorities , quality assurance staff confirming data integrity , and much more .
This knowledge work is about translating R & D data into decisions and documentation and requires answering questions and generating content in the natural language of scientists . Scientific large language models ( LLMs ) that are fine-tuned versions of popular general-purpose LLMs like GPT , such as BioGPT , or Llama , such as BioMedGPT-LM , have obvious potential .
But a generative pre-trained transformer built for scientific language isn ’ t enough . The real challenge is rethinking how the underlying R & D data are structured and managed . To automate knowledge work , these LLMs need to operate on top of data that comes from the lab , a foundation in which there are many problems today .
Large pharma companies often employ hundreds of software applications within R & D labs alone , leading to data silos and a lack of data standardisation and interoperability that make it excruciatingly difficult to apply AI effectively . At Benchling , we ’ re taking the same modern platform approach that has transformed how many businesses digitally manage their sales or financial data and applying it to R & D to make automation of knowledge work a reality .
AI will change how pharma competes
Another key element in the work of biopharma companies needs to be reinvented to make the promise of AI in BioTech a reality : how companies compete .
ProFluent Bio , a Berkeley-based BioTech , recently open sourced a novel , AI-designed , CRISPR-based , human gene editor . To open source such intellectual property was previously unthinkable . In an era where scientists working with AI can design many more drugs than we could possibly ever bring to market – imagine a world of drug abundance ! – the competitive focus will shift away from protecting intellectual property and toward speed to market and creating step changes in the efficiency of clinical trials and regulatory approvals .
This shift will , in turn , help solve the biggest issue in making AI-driven drug discovery even more powerful : access to data . The historic focus on intellectual property has created an industry culture that treats all experimental data as proprietary . Yet the success of AlphaFold2 and AlphaFold3 is entirely predicated on the public availability of protein sequences and experimentally-resolved structures . Progress in developing new foundation models will require data abundance .
Open-source software has long been a tenet of the tech industry , fundamentally altering the nature of collaboration and competition . If the biopharma industry wants to realise the benefits of AI , companies must work together to generate the data needed to power it .
Companies are already starting to collaborate on open-source software projects around managing data in areas like molecular modelling , connectivity to lab instruments and bioinformatics code . Pre-competitive collaboration can extend even further to how AI models themselves are built . Federated learning allows companies to update a shared global model without sharing their underlying datasets with competitors . This approach has already shown significant improvement in AI models for small molecules , and can likely have an even larger impact for large molecules if companies invest in it together .
The era of BioTech AI
The era of rational drug design – an atom by atom , computer-aided approach to designing drugs for a specific target – started more than 30 years ago . It had a tremendous impact , leading to breakthroughs for debilitating diseases like cystic fibrosis . We are now entering an era of AI-driven drug discovery , which promises to be AI ’ s greatest contribution to humanity by finding treatments for the thousands of currently untreatable diseases .
If the bulk of biopharma R & D continues to operate as it does today , a treasure trove of new drugs will be created that may never make their way to patients . Applying AI beyond drug discovery and using it to reinvent all elements of R & D will ensure that doesn ’ t happen . �
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