Abstract

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Title:: Application of Generative Deep Learning in Object-oriented Molecular Design

Author(s):: WANG Jifeng; WANG Ying; State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200438, China

Keywords:: Molecule design; generative deep learning; generative adversarial network; variational autoencoder; denoising diffusion probabilistic model; model evaluation framework; molecular representation

Abstract:: Designing molecules with specific functions within an immense chemical space is a fundamental challenge in chemistry and materials science, as traditional methods often lack efficiency and exploratory capacity. The advent of generative deep learning has introduced automated and intelligent approaches that promise to transform molecular design. In this review, we examine the advancements in applying generative deep learning to molecular design. We first compare molecular representation methods, including SMILES notation, molecular graphs, and three-dimensional structural representations, highlighting their respective advantages and limitations. We then critically evaluate three leading generative models: Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Denoising Diffusion Probabilistic Model (DDPM), focusing on their capabilities in property control, generalization ability, and synthetic feasibility. We also discuss the characteristics of the three models in object-oriented molecular design by analyzing their capability of molecular generation and property optimization. Finally, we propose future research directions for leveraging the generative models in molecular design, aiming to inspire further advancements in this rapidly evolving field.