Inverse Molecular Design

Inverse molecular design (IMD) aims to computationally design molecules with desired properties, accelerating drug discovery and materials science. Current research heavily utilizes deep learning, employing generative models like diffusion models, variational autoencoders, and reinforcement learning agents, often coupled with graph neural networks and large language models to handle molecular representations and incorporate multiple property constraints. These methods are being refined to improve the trustworthiness and efficiency of generated molecules, addressing challenges like exploring vast chemical spaces and ensuring the synthesized molecules are physically realistic and synthesizable. The ultimate goal is to significantly streamline the design process, leading to faster development of novel materials and therapeutics.