Application scenario-oriented molecule generation platform developed for drug discovery - Zheng, Lianjun; Shi, Fangjun; Peng, Chunwang; Xu, Min; Fan, Fangda; Li, Yuanpeng; Zhang, Lin; Du, Jiewen; Wang, Zonghu; Lin, Zhixiong; Sun, Yina; Deng, Chenglong; et al. (Methods (San Diego, Calif.), 2024)
[Display omitted] •Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, and active learning etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, and pharmacophore etc.), to enable customized solutions for a given molecular design scenario.•Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization.•We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations.•Remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics. Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.