A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design

TL;DR

This paper introduces MOSES, a benchmarking platform for evaluating deep generative models in molecular design, providing standardized assessments to compare architectures like RNNs, VAEs, and GANs in drug discovery.

Contribution

It presents a comprehensive benchmarking framework for deep generative models in molecular design, enabling fair comparison and analysis of different architectures.

Findings

Different architectures show complementary strengths across metrics

Trade-offs exist between exploration and exploitation in chemical space

The platform standardizes evaluation for future research

Abstract

The development of novel pharmaceuticals represents a significant challenge in modern science, with substantial costs and time investments. Deep generative models have emerged as promising tools for accelerating drug discovery by efficiently exploring the vast chemical space. However, this rapidly evolving field lacks standardized evaluation protocols, impeding fair comparison between approaches. This research presents an extensive analysis of the Molecular Sets (MOSES) platform, a comprehensive benchmarking framework designed to standardize evaluation of deep generative models in molecular design. Through rigorous assessment of multiple generative architectures, including recurrent neural networks, variational autoencoders, and generative adversarial networks, we examine their capabilities in generating valid, unique, and novel molecular structures while maintaining specific chemical properties. Our findings reveal that different architectures exhibit complementary strengths across various metrics, highlighting the complex trade-offs between exploration and exploitation in chemical space. This study provides detailed insights into the current state of the art in molecular generation and establishes a foundation for future advancements in AI-driven drug discovery.