Aug 14 2023
Data Analytics

What the Growth of Generative AI Means for Drug Discovery and Clinical Trials

An NVIDIA leader shares his perspective on generative artificial intelligence as a new prescription for success.

The introduction of ChatGPT directed the world’s attention to the power of generative artificial intelligence and its applications across industries. Though the potential of AI applied to drug discovery workflows has been known for years, the emphasis and excitement around large, generative models will significantly accelerate the adoption and validation of these tools for biological discovery. 

The early results demonstrate that the transition to generative models is an inflection point for the industry. Applications of large language models (LLMs) for target and generative lead identification are accelerating our understanding of biology and improving the quality of initial drug candidates. Higher-quality representations of drugs and their targets are today improving our ability to predict drug properties and their interactions.

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Exceed a Substandard Status Quo in Drug Discovery

Improving the speed and quality of early preclinical drug discovery pipelines is directly related to unlocking new therapies to ultimately improve patient outcomes and save lives. Traditional drug discovery is time-consuming and expensive. After a drug target is identified and optimized, the likelihood of getting to market successfully is less than 10 percent.

Even small advancements in time-to-lead optimization and improvements in the likelihood of clinical success are important to addressing the thousands of diseases that today have no known treatment or cure.

Large Language Models are Learning the Language of Biology

Today’s generative AI models understand the language of biology, chemistry and genomics, leveraging many of the tools that have been developed over the past several years that led to the advent of ChatGPT and other LLMs. Although these models had been applied to biopharma sequence data before (for example, evolutionary scale modeling trained on large databases of protein sequences), the demonstrated success of Google DeepMind’s AlphaFold was the seminal moment demonstrating the promise of these tools.

Since then, the exponential growth of research and development in the space has been hard to ignore: Virtually every week, there are new state-of-the-art architectures, models and approaches published and made available, from academia to industry. For example, DiffDock was recently released, which demonstrated the first viable AI-based small molecule protein docking tool. Where applicable, this model speeds up orders for docking workflows, leading to cheaper and more efficient small molecule screening workflows.

EXPLORE: What types of AI are being used in healthcare?

Generative AI Is a Prescription for Healthcare Success

Every step of the AI drug discovery pipeline is being accelerated with AI. LLMs sift through literature to discover novel druggable targets. Generative models trained on small molecule and protein data provide tools to virtually generate drug candidates with defined properties (and, in a recent joint paper between NVIDIA and Evozyne, have been synthesized and validated in the lab).

Representation learning workflows, such as those supported by evolutionary scale models in protein design, provide the highest-quality property prediction tools yet known in the field and directly contribute to the performance of structure prediction tools such as AlphaFold and ESMFold. Applications of these models to genomics have led to the first true generalizable foundation model for tasks such as gene expression prediction, recently published by NVIDIA, the Technical University of Munich and InstaDeep (acquired this year by BioNTech).

Generative AI’s impact on biology and drug discovery will soon improve the lives of people everywhere.”

We are at a major inflection point for the use of AI in early drug discovery, but the same is true for clinical trials. If we have great generative and predictive tools for drugs and their targets, how can we more effectively structure a clinical trial to maximize its likelihood of success for a given drug candidate?

Tools that integrate multimodal patient data, electronic health records, genomics, and other representations of human health and biology are being built today to more effectively and efficiently recruit patients into clinical trials and to help them find trials themselves.

Generative AI’s capabilities to understand complex relationships in natural language are now, for the first time, generally understood by hundreds of millions across the globe. Generative AI’s impact on biology and drug discovery will soon improve the lives of people everywhere.

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