Visualizing Deep Graph Generative Models for Drug Discovery

TL;DR

This paper introduces an interactive visualization framework for deep graph generative models in drug discovery, enabling real-time molecular visualization and optimization to enhance interpretability of AI-driven molecular design.

Contribution

It presents a novel visualization tool that allows for interactive exploration and real-time optimization of molecules generated by deep graph models in drug discovery.

Findings

Enables visualization of molecules during encoding and decoding processes.

Provides real-time molecular optimization functionalities.

Enhances interpretability of deep generative models in drug discovery.

Abstract

Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time consuming process. Driven by big chemical data and AI, deep generative models show great potential to accelerate the drug discovery process. Existing works investigate different deep generative frameworks for molecular generation, however, less attention has been paid to the visualization tools to quickly demo and evaluate model's results. Here, we propose a visualization framework which provides interactive visualization tools to visualize molecules generated during the encoding and decoding process of deep graph generative models, and provide real time molecular optimization functionalities. Our work tries to empower black box AI driven drug discovery models with some visual interpretabilities.