Towards LLM-Driven Multi-Agent Pipeline for Drug Discovery: Neurodegenerative Diseases Case Study

Gleb Vitalevich Solovev, Alina Borisovna Zhidkovskaya, Anastasia Orlova, Anastasia Vepreva, Tonkii Ilya, Rodion Golovinskii, Nina Gubina, Denis Chistiakov, Timur A. Aliev, Ivan Poddiakov, Galina Zubkova, Ekaterina V. Skorb, Vladimir Vinogradov, Nikolay Nikitin, Andrei Dmitrenko, Anna Kalyuzhnaya, Andrey Savchenko

Keywords: LLM, LLM agents, drug discovery, deep learning, computational chemistry, generative AI

TL;DR: The novel multi-agent approach for drug discovery has the potential to treat brain diseases. This system combines LLM-based task decomposition and specialized generative models to generate valid target molecules efficiently.

Abstract: Recent studies demonstrate that Large Language Models (LLMs) can accelerate scientific progress in chemistry and drug development. However, existing approaches have not achieved successful automation of the complete drug discovery pipeline, primarily due to the absence of comprehensive datasets and the limitations of single-model solutions. This paper introduces multi-agent approach that combines LLMs with specialized generative models and validation tools to automate the end-to-end drug discovery process. The key innovation lies in addressing the complex transition from natural language problem formulation to building a complete computational pipeline for real pharmaceutical research tasks. Experimental results demonstrate that our multi-agent solution achieves 92\% accuracy in end-to-end drug search complex tasks, significantly outperforming single-agent implementations. We validated the system's effectiveness on an original newly farmed dataset with tasks and full solutions for three pharmaceutical cases targeting neurodegenerative diseases (Alzheimer's, multiple sclerosis, and Parkinson's). The main contributions include demonstrating the advantages of a multi-agent LLM-powered approach for automating pharmaceutical drug design and validating its success on real-world drug discovery challenges.

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Submission Number: 23