A comprehensive review of molecular optimization in artificial intelligence-based drug discovery

Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence-based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping-based, molecular distribution matching-based, and guided search-based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence-based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.

Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

合理设计多电子转移机制实现更好的水系锌-有机电池

由于环境相容性好、分子结构可定制和有机物资源丰富等优势,水系锌-有机物电池(AZOBs)成为构建新一代大规模储能系统的关键技术.然而,电导率差、有机物溶解和单一活性基团等问题严重限制了有机物正极材料的倍率性能、稳定性和比容量.因此,具备多氧化还原中心和稳定骨架的有机物正极材料对于实现高性能有机物正极材料至关重要.这些多官能团有机物可协同作用并激发基于多电子转移的氧化还原反应,进而促进H^(+)/Zn^(2+)共嵌以显著提升电池性能.本文探索了多官能团有机物电极的分子结构与其氧化还原反应机理之间的构效关系.本文综述了多官能团有机正极材料在提高氧化还原电位、比容量、动力学以及稳定性等方面面临的挑战和解决策略,为进一步开发先进AZOBs的关键正极材料提供了重要基础.