De Novo Drug Design and Generative AI: Algorithmic Approaches to Novel Molecules
Manimegalai K · 2025
De novo drug design represents a paradigm shift in medicinal chemistry transitioning from traditional rule-based optimization to algorithmically generated molecular innovation. With the advent of generative artificial intelligence (AI), models such as variational autoencoders (VAEs), generative adversarial networks (GANs), reinforcement learning (RL), and diffusion architectures are transforming the exploration of chemical space beyond what human intuition or empirical enumeration could achieve. These models learn latent representations of chemical structures and generate novel compounds optimized for pharmacological, physicochemical, and synthetic feasibility constraints. This chapter explores the theoretical foundations and computational workflows of generative models in de novo drug design, emphasizing their integration with docking, QSAR, and molecular dynamics simulations. Current challenges including the lack of interpretability, synthesizability, and benchmarking uniformity are critically assessed, and emerging solutions leveraging multi-objective optimization, quantum generative models, and human-in-the-loop strategies are discussed. The chapter concludes by highlighting the evolving convergence of deep generative chemistry, AI-augmented medicinal design, and automated synthesis systems poised to redefine next-generation drug discovery.