Sponsored content brought to you by
If you’re wondering how to bring AI into your drug discovery lab beyond ChatGPT, you’re not alone. The multitude of solutions and providers creates analysis paralysis, as life sciences companies struggle to identify the best AI tools to solve their challenges.
As they seek greater innovation, faster and deeper insights, and greater productivity, many lab leaders ask such questions as:
- How do I get an integrated environment for analysis?
- How do I get a holistic view of R&D data?
- How do I plan workloads across instruments to balance utilization?
- How do I benchmark across laboratories to maximize ROI?
- How do I quickly search across journals and documents to get insights?
When done right, AI can reduce analysis and validation efforts by more than 50%, generating huge cost savings for R&D labs through higher productivity and data-driven recommendations for research analyses. That’s because AI has the capacity to process inhuman volumes and varieties of data, rapidly discern patterns, and then make predictions and recommendations based on the patterns detected and insights unearthed.
Reducing the failure rate in drug discovery is an excellent application for AI. Even though most molecules fail in the early stages, the R&D process generates valuable data and lessons learned. Scientists looking to significantly speed up their research can apply AI in numerous ways—whether analyzing the results of past experiments, exploring the literature, or modeling potential outcomes—to achieve their goals. The trick is to integrate AI with laboratory informatics solutions—such as LIMS—to support areas from discovery and lead optimization to bioanalytical, pre-clinical, and even manufacturing.
This allows researchers and other scientists to make their queries in a single platform, generating results from internal, external, and proprietary data. When advanced analytics are not integrated with R&D lab informatics, it leads to manual efforts in data handling—and the resulting increases in analysis time and risk of errors. An end-to-end digital solution makes all data—from R&D through manufacturing—available for greater product life cycle management, lab performance, and finished product quality.
This is important because the volume of data generated from just a single molecule or drug across R&D, manufacturing, and commercial divisions is staggering. Likewise, its potential value to future development deserves attention. AI can combine a company’s proprietary drug discovery and development data with troves of publicly available data—in a matter of minutes—to satisfy researcher’s queries.
To do this requires another type of advanced analytics—a local Large Language Model (LLM). While open source LLMs like ChatGPT are trained on public data stores, local LLMs access the company’s private drug discovery and development data to increase the relevance, accuracy, and specificity of the data in response to queries.
Open source LLM alone can be too general, or even prone to hallucinations, and can miss critical information when answering a question. A scientifically relevant local LLM, integrated in the lab’s data ecosystem, can be trained on a pharmaceutical company’s proprietary data sets, including discovery results, toxicology reports, PK reports, clinical trial protocols, regulatory reports, and more.
Another distinction of the local LLM is how it references the data it surfaces for users. While open source LLMs tend to reference at either the journal or institution level, a well-trained local LLM provides for every question, instead, a specific set of articles, delivering quick and relevant results for validation purposes.
The value proposition of AI using local LLMs is huge: it can deliver 10X faster responses to document searches, with significant improvement in the relevance and contextualization of the information surfaced. This allows organizations to create templates to accelerate their work across drug target identification, lead drug identification and optimization, PK modeling, SOP generation, protocol design, medical writing, market access analogues, pharmacovigilance assessments, and numerous other areas in the drug development continuum.
Alan Marcus is Chief Growth Officer at LabVantage Solutions and Krish Ghosh is President at TCG Digital.
To learn more about AI in laboratory informatics, visit www.labvantage.com/informatics/analytics
