We wish to explore the use of Large Language Models for biological research, using target discovery and validation as the initial use case. Target discovery was picked as a use case because it is a common process in all pharmaceutical R&D businesses that requires mining of large volumes of information. We plan to use prompt-tuned LLMs on a highly structured public data resource for the Retrieval-Augmented Generation (RAG) of plain English answers to the typical research questions asked in target discovery. Expected project outputs are a set of guidelines for the most advantageous use of LLMs in research and an open-source target discovery pipeline with prompt-tuned Large Language Models.

Problem Statement

Large Language Models (LLMs) exemplified by ChatGPT 4, attracted a lot of attention recently. However, the best use cases for LLMs in the R&D setting are not well understood, and there is no consensus yet on what a realistic pre-competitive project in this space could be. As an initial use case we propose to create an open-source system for target discovery based on public data and pre-trained LLMs. Target discovery is a common and critical task in drug discovery that typically requires complex data mining of ever-increasing body of knowledge, and placing proprietary research results into the context of public information.

Value Proposition and Expected Results

• The proposed approach would allow for natural language queries to be effectively translated into structured queries, executed over standardized data sources (such as, for instance, Open Targets), and converted into human-readable outputs.

• The project does not require participating companies to disclose any of their proprietary data. However, they can mine their proprietary data by using private instances of the described pipeline.

• One significant expected outcome includes lessons learned on the best practices for deployment, prompt-tuning, fine training, and limitations of applicability of LLMs for research purposes. We will seek to publish these lessons learned for the benefit of the research community.

• Another significant outcome can be an open-source target discovery pipeline prototype itself.

• Improved efficiency and accuracy in target discovery and validation.

• Creation of a framework that can be used for other use cases:

  • A model of project execution for other pre-competitive core model work.

  • Additional prototypes for other common discovery tasks can be created if/when more suitable use cases are identified.

Alignment with the Pistoia Alliance Strategic Priorities

This project is part of the Artificial Intelligence at Scale strategic priority.

Project Scope

In scope:

Preparatory steps:

• Define the most common research questions in target discovery and validation. Establish an agreement between the project team that these are indeed the core target discovery business questions, and rank order them by vote by perceived relative importance. If such questions are many, pick the top ones. Establish an agreement on how many exactly. One can use this paper as a starting point for listing of relevant competency questions: https://www.sciencedirect.com/science/article/pii/S1359644613001542 (Failure to identify business questions, or picking too many or too few is a project risk)

• Open Targets can serve as a publicly available standardized data source for this use case. Validate that Open Targets either has a ready to use Knowledge Graph implementation, or can be converted into a KG with reasonable cost (this is known project risk)

• Select a Large Language Model engine from publicly accessible sources. (Failure to identify a suitable open LLM is a project risk)

Prompt-tuning procedure:

• Define a plain text data source for mining; one of the choices can be the entire set of paper abstracts indexed in PubMed plus perhaps the entire collection of open-source papers. (Failure to download this large volume of data can be a risk)

• Ask a plain English question from the collection of business questions identified above

• An LLM uses the question and the data source to produce a human-readable answer

• Either same or some other LLM converts this answer to a Knowledge Graph. (KG generation from text is a source of risk)

• This answer Knowledge Graph is compared to the KG of the original data source (such as Open Targets). This comparison must be local; in other words, irrelevant sections of the larger knowledge graph should not be considered. (Ability to find a ready KG comparison algorithm or to code it fresh is a risk).

• An accuracy metric is computed + inputs and outputs saved in some DB

• Prompt-tune the opening plain text question to maximize the output quality

• Repeat this tuning cycle for all business questions in the collection

• Use these optimized prompts in RAG, below

Retrieval Augmented Generation (RAG):

• Ask plain English question using prompt-tuned version of one of the questions from the business questions collection

• This question is converted into a structured query by an LLM (Failure to generate a proper query for a KG database system is a risk)

• Execute this query over a structured controlled data source (e.g. Open Targets DB)

• Convert raw output of the query into human-readable input by an LLM

• Quality assurance: it is desirable to produce a few instances of questions with known answers, ideally, by human experts, and then use these question-answer pairs as sanity checks of the RAG pipeline

Not in scope:

• Proprietary data are not used to train any model and project participants are not asked to share proprietary data in any way. But proprietary data may be analyzed in the context of this set-up by the individual participants using private instances of the pipeline.

• Training of a brand new LLM is out of scope. The plan is to only prompt-tune an existing LLM.

Fine training of an existing LLM on a body of biomedical knowledge is generally out of scope, but may be considered as a project extension or option, if sufficient quality of results cannot be achieved with prompt-tuning only, and if finances allow.

Project Phases and Milestones

Risk Registry

Project Stakeholders

Sponsors:

• Lars Greiffenberg, Abbvie

• Brian Martin, AstraZeneca

Project Participants:

Stakeholder mailing list in Google Groups: https://groups.google.com/a/pistoiaalliance.org/g/llm-project

Meetings

Every other week at 8 am PST (= 11 am EST = 4 pm London = 5 pm Berlin) starting on January 10th, 2024

• 2024.01.10 Recording (Passcode: P#69H5dm) Slides

• 2024.01.24 Recording (Passcode: t!O5T38a) Slides

• 2024.02.07 Recording (Passcode: L58@v7Dg) Slides | Slides from the talk by Sebastian Lobentanzer

• 2024.02.21 Recording (Passcode: B59W3wT+) Slides

References

1. https://www.sciencedirect.com/science/article/pii/S1359644613001542

2. https://www.nature.com/articles/s41573-020-0087-3

3. https://www.epam.com/about/newsroom/press-releases/2023/epam-launches-dial-a-unified-generative-ai-orchestration-platform

4. https://epam-rail.com/open-source

5. Open LLM Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

6. Chatbot Arena: https://chat.lmsys.org/?arena

7. Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

   https://arxiv.org/abs/2310.01061

8. Knowledge-Consistent Dialogue Generation with Language Models and Knowledge Graphs

   https://openreview.net/forum?id=WhWlYzUTJfP&source=post_page-----97a4cf96eb69--------------------------------

9. BioChatter Benchmark Results: https://biochatter.org/benchmark-results/#biochatter-query-generation

10. MBET Benchmark (embeddings) https://huggingface.co/spaces/mteb/leaderboard

11. Lora-Land and Lorax: https://predibase.com/lora-land