Emerging Application Progress of Deep Generative Models in De Novo Drug Design

Recently, deep generative models (DGMs) have become revolutionary tools in de novo drug design, allowing for the quick exploration of large chemical spaces that are not possible with conventional techniques. In contrast to traditional high-throughput screening methods, DGMs like reinforcement learning-based frameworks, generative adversarial networks (GANs), and variational autoencoders (VAEs) can create novel molecular structures with desired pharmacological characteristics in silico. These models are able to capture intricate structure–activity relationships and produce candidate molecules with optimal drug-like properties, such as solubility, bioavailability, and target specificity, by utilizing extensive chemical and biological datasets. Advances in multimodal generative approaches, which integrate textual, biological, and chemical data, further improve the ability to suggest compounds with therapeutic relevance and structural novelty. Modern developments have also included human-in-the-loop systems, in which medicinal chemists direct the generative process to guarantee synthetic accessibility and practical viability. In order to improve the interpretability of model decisions and promote trust and adoption in pharmaceutical research, explainable AI techniques are also being developed. Applications show accelerated lead discovery pipelines and span a variety of therapeutic areas, such as neurological disorders, oncology, and antibiotic resistance. Notwithstanding these developments, problems with data quality, model generalization, and establishing a connection between in silico predictions and experimental validation still exist. Future studies will focus on integrating multi-objective optimization, physics-based simulations, and personalized medicine frameworks. All things considered, DGMs have a great deal of potential to transform contemporary drug discovery, lower expenses, and speed up the discovery of innovative treatments for complicated illnesses.