Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study

TL;DR

This paper proposes an evaluation framework for AI-based multi-target drug design, using brain diseases as a case study, to standardize assessment and compare different generative models and algorithms.

Contribution

It introduces a comprehensive benchmark suite for assessing AI-driven molecular design in multi-target drug discovery, specifically for brain diseases.

Findings

Both evolutionary algorithms and generative models achieve competitive results.

The framework enables standardized comparison of AI techniques in drug design.

The methodology integrates target selection, data preprocessing, and property prediction.

Abstract

The widespread application of Artificial Intelligence (AI) techniques has significantly influenced the development of new therapeutic agents. These computational methods can be used to design and predict the properties of generated molecules. Multi-target Drug Discovery (MTDD) is an emerging paradigm for discovering drugs against complex disorders that do not respond well to more traditional target-specific treatments, such as central nervous system, immune system, and cardiovascular diseases. Still, there is yet to be an established benchmark suite for assessing the effectiveness of AI tools for designing multi-target compounds. Standardized benchmarks allow for comparing existing techniques and promote rapid research progress. Hence, this work proposes an evaluation framework for molecule generation techniques in MTDD scenarios, considering brain diseases as a case study. Our methodology involves using large language models to select the appropriate molecular targets, gathering and preprocessing the bioassay datasets, training quantitative structure-activity relationship models to predict target modulation, and assessing other essential drug-likeness properties for implementing the benchmarks. Additionally, this work will assess the performance of four deep generative models and evolutionary algorithms over our benchmark suite. In our findings, both evolutionary algorithms and generative models can achieve competitive results across the proposed benchmarks.