The recent rise of generative Artificial Intelligence (AI) has revolutionized the approach to innovative solutions for the Life Sciences industry. ChatGPT (GPT-4), an advanced large language model powered by AI, may have the ability to greatly benefit the pharmaceutical industry. With its ability to understand and generate human-like text, the industry may one day be able to leverage ChatGPT to streamline operations, enhance R&D efforts, improve customer support, and aid in regulatory compliance.
Medical Affairs: Insights Curation
Commercial: Call Notes Insights
Drug Development: Comparing Protocols
R&D (Research & Development)
Learning Solution: Personalized Learning Experience
Imagine PharmaCorp, a hypothetical leading pharmaceutical company committed to accelerating the drug discovery process to address critical healthcare needs. They have identified the potential of ChatGPT to augment their efforts and optimize their research and development pipeline.
- Trial Optimization: PharmaCorp’s scientists and researchers can potentially utilize ChatGPT to conduct comprehensive literature reviews. They can input specific keywords or phrases related to their drug discovery project and ask ChatGPT to summarize relevant scientific papers, patents, clinical trial results, and other pertinent information.
- Drug Development: Developing new drug molecules with desired properties is a complex and time-consuming task. ChatGPT could have the potential to assist medicinal chemists at PharmaCorp by generating novel molecular structures based on specified parameters and constraints. Chemists can communicate with ChatGPT, describing the desired properties of a drug, and the model could theoretically provide suggestions and variations for molecular structures that meet the given criteria.
- Patient Interaction: PharmaCorp can deploy ChatGPT to help enhance their customer support services and likely provide accurate and reliable information to patients. ChatGPT has the potential to address frequently asked questions about medications, dosages, potential side effects, and drug interactions. It can also provide general health and wellness advice based on trusted sources.
- Regulatory Approval: ChatGPT can prospectively help streamline the process of preparing regulatory documents and responding to inquiries from regulatory authorities. By providing key information and context, ChatGPT may be able to assist in drafting sections of drug submissions, safety reports, and responses to regulatory queries, ensuring accuracy, consistency, and compliance with guidelines.
But how did AI advance to this level of accessibility and capability?
Large language model accessibility of AI
The advancement of large language models has transformed the accessibility of artificial intelligence, making it widely available for everyday usage. Natural language processing (NLP) is a field of AI that deals with the interaction between computers and human (natural) languages.
In conjunction with the rise of NLP and AI, comes the rise of popularity of unstructured data. Unstructured content, in contrast to structured data previously mentioned, is data that does not have a predefined structure, such as text, images, and videos. Processing unstructured content is challenging because it requires different techniques than processing structured data.
ChatGPT and its key features
| Pre-Training | Generative Model | Contextual Understanding | Large-Scale | Fine-Tuning | Transfer Learning |
| ChatGPT is pre-trained on a large corpus of text data, allowing it to generate human-like text. |
ChatGPT is a generative model, meaning it can generate new text based on the input it receives. |
ChatGPT has the ability to understand context and generate relevant responses. |
ChatGPT is a large-scale language model, with over 175 billion parameters, which gives it the ability to generate diverse and nuanced text. |
ChatGPT can be fine-tuned for specific tasks, such as answering questions or translating text by training it on smaller, task-specific datasets. |
ChatGPT can leverage its pre-training to quickly adapt to new tasks, making it an effective tool for natural language processing tasks. |
ChatGPT is Analogous to Unstructured Data, just as SQL is to Relational Data
ChatGPT, as a language model, is designed to handle unstructured data in the form of natural language conversations. It can understand and generate human-like responses based on the input it receives. For example, researchers can utilize ChatGPT to analyze a vast collection of research papers and extract key findings or trends. Companies can process protocols and identify patterns or best practices. ChatGPT's assistance enables users to navigate through unstructured data in a more efficient and effective manner, facilitating decision-making and driving innovation.
Overall, ChatGPT's capabilities in processing unstructured data have streamlined the information retrieval process, eliminated the need for fixed schemas, and empowered users to uncover insights and make meaningful connections within large volumes of unstructured information.
On the other hand, SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. SQL allows users to query and retrieve specific data from these databases by specifying structured commands.
Effective prompt engineering plays a vital role in unlocking the full potential of generative use cases for ChatGPT. By crafting precise and context-rich prompts, developers can harness the model's creative capabilities to generate imaginative and personalized outputs. Whether it is generating unique stories, creative writing prompts, or dynamic conversational experiences, prompt engineering empowers developers to create engaging and interactive applications that leverage ChatGPT's generative nature
Drawing a parallel, ChatGPT can be seen as an intelligent system capable of understanding and generating responses from unstructured data, like how SQL enables users to extract meaningful information from relational databases. Both ChatGPT and SQL play crucial roles in dealing with their respective data types and facilitating efficient data retrieval and analysis.
Here is a comparison between data processing, storing, search, and querying in a relational database using SQL and an unstructured database utilizing ChatGPT:
| Aspect | Relational Database (SQL) | Unstructured Database (ChatGPT) |
| Data Structure | Structured and predefined schema | Unstructured, no fixed schema or column names |
| Data Processing | SQL queries and structured operations | Natural language processing and understanding |
| Data Storage | Tables with rows and columns | Text-based storage and document-like representations |
| Search and Query Capabilities | Structured queries and joins | Natural language queries and semantic understanding |
| Flexibility | Requires predefined schema and design | Adapts to various data types and structures |
| Handling Unstructured Data | Challenging without preprocessing | Handles unstructured data with little preprocessing |
| Insights and Relationships | Extracts relationships using joins | Discovers relationships based on contextual analysis |
| Scalability and Processing Power | Efficient for structured data | Can process and analyze large volumes of text data |
| Usage Complexity | Requires knowledge of SQL and schema | Intuitive and accessible through conversational AI |
The power of generative AI and ChatGPT presents an exciting time for AI. Specifically for the pharmaceutical industry.
By integrating ChatGPT into their drug discovery and development processes, the hypothetical leading pharmaceutical company PharmaCorp, can leverage its language processing capabilities to accelerate research, improve molecular design, and enhance patient support and education. ChatGPT's ability to understand and generate human-like text empowers PharmaCorp's scientists, researchers, and customer support teams, leading to increased efficiency, reduced costs, and ultimately, the development of safer and more effective medications for patients in need.
Appendix
"Evolution Of Big Data In Modern Technology." Prompt Cloud, 4 Jun. 2020, www.promptcloud.com/blog/big-data-evolution-technology-modern/.
Authors: Sayee Natarajan, Nataraj Dasgupta, Ravindra Shukla, Viji Karunakaran, Becca Toskey, Susanna Helton
